Who You Are Matters: Bridging Interests and Social Roles via LLM-Enhanced Logic Recommendation
Authors: Qing Yu
Xiaobei Wang
Shuchang Liu
cheng.feng
Xiaoyu Yang
Xueliang Wang
Chang Meng
Shanshan Wu
HailanYang
Bin Wen
Huihui Xiao
Xiang Li
Fan Yang
Xiaoqiang Feng
Lantao Hu
Han Li
Kun Gai
Lixin Zou
Venue: NeurIPS 2025 poster ()
Keywords: recommendation with large language models
large language models
recommender systems
user roles
collaborative filtering
Primary area: deep_learning

Abstract: Recommender systems filter contents/items valuable to users by inferring preferences from user features and historical behaviors. 
Mainstream approaches follow the learning-to-rank paradigm, which focus on discovering and modeling item topics (e.g.,
categories), and capturing user preferences on these topics based on historical interactions.
However, this paradigm often neglects the modeling of user characteristics and their social roles, which are logical confounders influencing the correlated interest and user preference transition. 
To bridge this gap, we introduce the user role identification task and the behavioral logic modeling task that aim to explicitly model user roles and learn the logical relations between item topics and user social roles. 
We show that it is possible to explicitly solve these tasks through an efficient integration framework of Large Language Model (LLM) and recommendation systems, for which we propose TagCF. 
On the one hand, TagCF exploits the (Multi-modal) LLM's world knowledge and logic inference ability to extract realistic tag-based virtual logic graphs that reveal dynamic and expressive knowledge of users, refining our understanding of user behaviors.
On the other hand, TagCF presents empirically effective integration modules that take advantage of the extracted tag-logic information, augmenting the recommendation performance.
We conduct both online experiments and offline experiments with industrial and public datasets as verification of TagCF's effectiveness, and we empirically show that the user role modeling strategy is potentially a better choice than the modeling of item topics. 
Additionally, we provide evidence that the extracted logic graphs are empirically a general and transferable knowledge that can benefit a wide range of recommendation tasks. Our code is available in https://github.com/Code2Q/TagCF.

Who You Are Matters: Bridging Topies and Social Roles via LLM-Enhanced Logical Recommendation
Qing Yu Xiaobei Wang". Shuchang Liu?, Yandong Bai?, Xiaoyu Yang?, Xueliang Wang", Chang
Meng", Shanshan Wu², Hailan Yang". Bin Wen Huihui Xiao² Xiang Li Fan Yang', Xiaoqiang Feng', Lantao Hu? Han Li?, Kun Gaf Lixin Zou l.f
Wuhan University' Kuaishou Technology
(yu_qing, zoulixin www.iiiii@wwuueeee @whu. cn,
(wangriaobei03, Liushuchang, wushanshan03, , yanghailan, wenbin, ziachuihui, lixiang44, yangfan, fengriaooiiang hulantao, 1ihan08 Okuaissuu. com, gai kww.quuueqqqcoo com
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Abstract
Recommender systems filter contents/items valuable to users by inferring prefer- ences from user features and historical behaviors. Mainstream approaches follow the learning-to-ras paradigm, which focuses on discovering and modeling item topics (e.g., categories) and capturing user preferences for these topics based on historical interactions. However, this paradigm often neglects the modeling user characteristics and their social roles, which are logical confounders influencing the correlated interests and user preference transition. To bridge this gap, we introduce the user role identification task and the behavioral logic modeling task that aim to explicitly model user roles and learn the logical relations between item topics and user social roles. We show that it is possible to explicitly solve these tasks through an efficient integration framework Large Language Model (LLM) and recommendation systems, for which we propose TagCF. On the hand, TagCF exploits the (Multi-mon modal) LLM's world knowledge and logic inference ability extract realistic tag-based virtual logic graphs that reveal dynamic and expressive knowledge of users, refining our understanding of user behaviors. On the other hand, TagCF presents empirically effective integration modules that take advantage of the extracted tag-logic information, augmenting the recommendation performance. We conduct both online experiments with industrial environment and offline experiments on public datasets to verify TagCF's effectivvness,, and we empirically show that the user role modeling strategy is potentially better choice than the modeling of item topics. Additionally, we provide evidence that the extracted logic graphs are empirically general and transferable knowledge that can benefit wide range of recommendation tasks. Our code is available in https: /tt::/gitttt thub. com/Code2Q/Tag(.
1 Introduction
Recommender systems have become an indispensable tool to mitigate information overload and are commonly employed on various online platforms, from e-commerce to video streaming, assisting
users finding personalized content. Traditional recommendation systems [43] 55] 24 typically
adhere the learning-to to-rank paradigm, which leams the representation vectors of user and item
Work done during an internship at Kuaishou Technology.
i Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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based on the assumption that "similar users exhibit similar behavior", where these vectors can be interpreted latent topic distributions, analogous those in Latent Dirichlet Allocation (LDA) distribution. The cornerstone of this paradigm is the discovery and modeling of the item topics (e.g., categories) and how to capture user preferences for these topics based on historical interactions.
Similar
Similar
Item
User
Item
(a) ID-ID Collaborative Filtering
KGLLM
Matching
Confounder
Confoander
Live Music
Instrument
Folk Music Shows
Music Fathusiast
Music Crvation
logie
logic
Headphone
Symphoulst
Instrumentt
Item Tag
U2I lugic
User Tag
12U legic
Item Tag
(b) Tag-logic Collaborative Filtering
Figure 1: The toy example of the progress from traditional methods to tag-logic modeling.
Although effective, this paradigm neglects the modeling of user roles/characteristiss and thus fails to capture the logical relationships between user roles and item types, which potentially restricts the expressiveness of the resulting recommendation model. On one hand, existing solutions may discover item-item correlation candidates that are hard to interpret by item types, but the user's 's role and personal characteristics may serve as the confounders, providing meaningful explanations for these correlations. representative real-life example the famous diaper-beer correlation (46). where decent amount of human effort has been engaged to find that the "dads with newborns" are the logical explanation for the co-purchase behavior. On the other hand, iiterest-baseed modeling mostly relies on interest relations, while the user-item logic relations (e.g., certain type of user likes a certain type of item) can be far more interpretable and expressive. Consider the intuitive example in Figure D we observe user who purchases violin after consuming headphone. While a statistical model may find the molin-headdhone relation insignificant, the user may happen to be a edge and the symphonist-instrume
symphonist, where both the haapphonist-eadpphoon strong and general logical connections. As we will show in section 4.2) knowing the general logic in the real world and what role user plays in real life may significantly improve the recommender's ability to engage in more accurate interest exploration 35
edge
To mitigate the aforementioned limitations, we argue that the recommender system should complement the existing problem formulation with the following two tasks:
New Problems:
The user role identification task that constantly identifies and models what roles the user plays in the real world (e.g.. "dads with newbors" and "symphonist"), different from pre-defined accessible user profile features like gender and age;
The behavioral logic modeling task that models how user roles logically connect to the correspond- ing item topics. For this task, we further focus on two types of logic to align with the collaborative filtering paradigm as in Figure I: 1) for given user role, determine what types of items (also
referred to as "topics") ") are suitable or interesting (i.e., the U2I logic); And 2) for a given item topic, determine what kind of users would benefit from this content (i.e., the I2U logic).
for practical and privacy concerns [8]. user role identification is systematically challenging. for irresponsible, inefficient, and likely to be offensive to directly ask users to provide their social roles in many web services. Even if the users are willing to provide this information for mutual benefits, there is no guarantee that the provided features are accurate and comprehensive. 2) In terms of the logic modeling task, there have been some pioneering works that use eser-generated hashtags or causal tag discovery methods with the help of human experts [7 49 50]. However, these methods do not accommodate the scale of industrial recommender systems. Furthermore, they heavily rely on high-quality but manually designed variables, which restricts the model's 's expressiveness in large scale. Ideally, we would like to achieve an automatic modeling framework that can provide an immersive experience where the user roles and the task- logic patterns are modeled without bothering the users.
Challenges:
1) Different from item topic modeling 22

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Solution Framework: Fortunately, Multi modal Large Language Models (MLLMs) and Large Language Models (LLMs) have made significant breakthroughs T 64
demonstrating extensive world knowledge memorization abilities and advanced causal and logical reasoning capa- bilities [53 65]. which open the opportunities to reexamine the collaborative filtering framework's ability to model user roles and user behavioral logic. To this end, we propose general solution framework TagCF that simultaneously solves the aforementioned tasks and improves the recommen- dation performance. Specifically,, we first design task-specific tag identification module utilizing an MLLM (i.e., M3 (8) to extract related user (role) tags and item (topic) tags for each given item, based on the cemantic-rich and item tags, we propose a virtual collaborative logic filtering module that uses another LLM (i.e., Qwen2.5-7B (56]) to iteratively infer the U2I and I2U logic. To meet the scalability demand of the industrial environment, we propose several techniques, including cover set reduction and tag-logic model distillation. As will discuss in Section 4.2.3 this logic graph presents general behavioral logic that can be transferred to other recommendation tasks.
multi-modal features. Then, starting from the identified set of user tags
Finally, the generated tag knowledge and the logic graph are integrated as enhancements for standard recommendation frameworks with three empirically effective designs: 1) For model architecture, we enhance item representations with tag-based item encoder and propose separate tag-based user encoding design to fulfill the user role identification task; 2) For learning augmentation, we further show that can contrastive learning (CL) framework to integrate tag semantics into item and user representations: 3) During inference, we extend the recommendation model with a tag-logic inference score, which simultancously boosts the recommendation accuracy and diversity.
Empirical Support:
To verify the effectiveness of the tag extraction, the collaborative logic reasoning. and the recommendation enhancement framework, we conduct extensive experiments in an online A/B environment, an industrial offline dataset, and two public datasets. We also provide empirical findings on the different behaviors of user roles and item topics, ablation studies model variants, and sensitivity analysis of hyperparameters.
2 Related Work
2.1 Collaborative Filtering
Collaborative filtering (CF), one of the most successful recommendation approaches, continues to attract interest in both academia and industry. Over time, CF has evolved from traditional
methods 44 33 7
38 to advanced techniques incorporating sequences [25 29 and graph
structures 51 24
Among the representative methods, matrix factorization (MF) techniques [ 38
are effective in learning latent user and item representations. Sequential CF methods extend this
by modeling the temporal order of user interactions with Recurrent Neural Networks [25] and
Transformers 229 48 34). Graph-based CF methods like NGCF (51) and LightGCN 24 have also
gained attention in recent years. Besides, self-supervised learning approaches [58 9 60) have been
explored to enhance CF by learning robust representations. However, these methods often ignore user roles and logical relationships between characteristics.
Meanwhile, some personality filtering methods incorporate user traits through neighborhood filtering 33 16 [5] or matrix factorization extensions (30 16]. In the literature of psychology [2]], the majority of the works used the Big-Five personality model to represent the user's personality, while the choice of the most suitable personality definition that satisfies the requirements of the rec-
ommendation application still needs further investigation. Recent works [32 57 45) have attempted to leverage LLMs for personalized recommendations and user interest interpretation. While progress has been made, existing approaches still overlook explicit modeling of user roles and their logical relationships. In this work, we aim to address these gaps by bridging topics and social roles via LLMs-enhanced logical recommendation within the CF framework.
2.2 LLM-based Recommendation
L.M--enanced Recommender. Many current works have explored how to apply the LLM to generate auxiliary knowledge for enhancing traditional RS. LLMRG (52) fabricates prompts to construct chained graph reasoning from L.M to augment the recommendation model. L.MHG [22 first leverages LLMs to deduce Interest Angles (IAs) and categorize movies into multiple categories
3

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ASR
Logk Graphe
1o 15.1
G (Fau F..
Information
F
Mide Prrracue User Tog Set C
MILMM
Gea-
G
(C2u .2.1
La Jual
Cg (egu mn Fatt
HeemTag Estraction Prompt
Use Eatraction Promutt
Shows
ItemTing Set
Laute Gragh
Deea Ove Enoxkr
MLLM
LLM
Iaatuman
Masic Eafizonn Userlang 7
C'
Shones
Iten
Lagle Prompt X,
Prompt X
Alfay (1- 4lfia
Stap
Toe Tegloood I lorwing Augromationn
MLLM-based Tag Extraction
LLM-based Collaborative Logic Reasoning
Logle Integration
Figure 2: The main framework of the proposed TagCF.
within each IA to construct multi-view hypergraph. SAGCN 36 uses blainnbbsed prompting strategy to extract semantic interactions from LLM for each review and introduces semantic aspect- based graph convolution network to enhance the user and item representations by leveraging these semantic aspect-aware interactions. L.LM-KFRRc [62] uses L.MM to identify the complementary relationships of item knowledge graph. Subsequently, they train an entity-entiy--tem weight decision model which is then used to inject knowledge into the ranking model by using the real exposure and click feedback of complementary items. Nevertheless, current methods of using LLM for data enhancement primarily focus on the meta-features, neglecting knowledge from the user side and the logic rationale between user-item interactions. This limitation hinders their ability facilitate traditional recommenders to capture semantic and representative collaborative information.
LLM as Recommender Itself. Recently, LLMS have demonstrated remarkable performance a wide range of recommendation tasks. P5 (00 and M6Rec [13] finetune L.M by modeling recommendation tasks natural language processing tasks. ChatRec [17] employs LLMs
recommender interface for conversational multi-round recommendations. TALLRec [6] designs eustomized parameter-efffcciet tuning process for recommendation tasks on LLM with LoRA architecture. HLLM [ uses an item LLM to encode text features, feeding its embeddings to user LLM for recommendations, Compared to LLM-enhanced recommender, this paradigm's computational cost (both for training and inference) is much higher and the industry-deployaie solution is still an open question [14. As we will discuss section [3..1] this research direction focuses on the improvement of sequential models, which is complementary to our proposed knowledge extraction and augmentation framework.
3 The TagCF Framework
We present the task formulations of the standard top-N recommendation task, the user role (and item topic) identification task, and the behavioral logic reasoning task in Appendix A.1 The key notations in this paper are listed in Appendix A.2
3.1 MLLM-based Item-wise Tag Extraction
For given item I, we first take the original multi-modal information (e.g., audio, image, and title of videos) and use a multi-modal LLM (MLLM), M3 (8), to generate a semantic item embedding E; and initial textual features. Then, we use the textual features to construct corresponding prompts X? for user role tag extraction and X for item topic tag extraction (with prompt details in Appendix Given E, as auxiliary information, the given prompt will guide the generation of tags:
A.3)
T. (3 E.).C. M3(X E.).
(I)

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where T: and C are inferred user tags and item tags, and they are stored as static features of the given item i. In contrast, we assume that both the total user role tag set T and the total item topic tag set C change dynamically, so we apply update rules 'T + TUTT Ti and C < C U C, on daily basis.
Unrestricted Tags and Cover Set Reduction: A critical challenge of the application of Eq.(T is the unrestricted open-world generation of tags that may gradually accumulate excessive tag sets, while the tag frequency could be extremely skewed (see Appendix C). To circumvent this problem, we propose greedy and dynamic version of the min cover set finding algorithm (see Appendix (A.4) to automatically find a small subset of expressive tags (i.e., the cover set) that provide sufficient coverage of items and are mutually different in semantics. We denote the resulting cover sets of the two tag types as T and C*. In practice, we find that the cover set has some nice features in stability, generality, efficiency. and expressiveness (see Appendix C.T.. All these features add up to the effectiveness of the extracted knowledge.
Computational Bottleneck and Distillation: In practice, another key challenge is the computational cost of the MILMs, especially when there is large number of newly uploaded items to process on daily basis (e.g. videos and news). As a countermeasure, we propose to apply Eq.(I) on a smaller subset (tens of thousands) of newly uploaded items, train efficient distilled models Po (d I x T + [0, 1] and (o(c))) I x C - [0, 1J based on the sampled data, then use 0 to predict user/item tags for all items (in millions). We provide the algorithmic details of this procedure in Appendix AA.4 In section 3.3] we show that 0 may also participate in the recommendation model training. providing better alignment with user interactions.
3.2 LLM-based Collaborative Logic Filtering
With the daily update of T and C, we use an LLM (i.e., QWen2.5-7B (56]) to update and maintain the two graphs G and g1æU. Specifically, we iteratively select the tags that have not been included in the (source nodes of) logic graphs, construct the two logic reasoning prompts (in Appendix A.3). then obtain the I2U logic and U2I logic with:
To LLL((X);;CC LLM(X.),
where X. and X, are imput prompts for item tag E C and user tag t E T, Te and C, are generated tags. correspondingly. To keep the tag set update inclusive, we also update the tag sets with T + TUTT Te and C & CUC, on a daily basis. Both [T) and Eq. use the pretrained model without finetuning in order to keep the intact world knowledge and reasoning ability, and we find the generation sufficiently accurate according to human expert justification (Appendix (DI).
Distill Logic within Cover Sets: As we have mentioned in section 3.1 we can achieve a stable and general inference using the cover sets T and C*. However, Eq 2 does not guarantee a generation output within the cover sets. As countermeasure, we learn distilled models P. (c(() and (tlc) on the full tag sets with the LLM-inferred data generated by Eq.(2). then predict the logic connections between the cover sets T and C*, where the predicted scores are used to select top-b target tags for each given input tag. We present the details of this process in Appendix A.5 and denote the resulting graph as gUZl= and gizu., Additionally, as we will verify in Section 4.2.3, these graphs are transfer-friendly as they use tags of general concepts and each logic represents a real-world task-agnostic user behavioral logic, taking advantage of the LLM.
3.3 Tag-I.ogic Integration in Recommendation
Note that item tags and user tags emphasize different semantic aspects, can implement two corresponding integration alternatives with symmetric design and we denote them as TagCF-it (that uses item tags to infer) and FagCF-ut (that uses user tags infer). Without loss of generality, we introduce TugCF-ut with three effective augmentation methods in the following sections, and provide detailed specifications in Appendix A.6
3.3.1 bag-based Encoder
Item Encoder:
For each item i, we first obtain user tags Te and item tags C, through (or Eq.(D)
Then, the embeddings of all extracted tags T, fc.lt E T (or C, {foelc C,) in TFgCF-it)
aggregated through either Mean pooling or an Attention Mechanism [61] (the latter adopted in
practice), generating the tag- tag-based item encoding r.0) Rd (or r (c)
e Rd) These encodings provide
5

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semantic information that may augment the standard ID-based item embedding x, E R We provide the details of our attention operation in Appendix[A.5
For each user u, we first obtain the user's interaction history Hu as input. Then, use sequential models (ie., SASRec [29]), t/z and thy, that separately encode the IDDbaaee item embeddings and the tag-based item embeddings for the history, and denote the resulting user
User Encoder:
encodings X and (f) (TagCF-it generates ru instead). Subsequently, merge these two
embeddings and obtain the enhanced user representation:
Φn. MLP
Vsa (x, ① r(
(3)
where @ is the concatenation operation. Finally, we calculate the predicted score as: Drow (u, i) = P(jlu) = Sigmoid
(4)
During training, each user history is associated with set of interacted items I, as positive targets, and we randomly sample negative item i for each it E Iu For each training sample (u, it, i the learning objective is defined the combined binary cross-entropy loss:
Cu (t (i)) W log P(i [u) log(1 - P(i
(5)
where W; denotes the reward weight of the positive item. Intuitively, the combined user rep- resentation ensures the tag-aware encoding for both items and users, which improves the model
expressiveness and recommendation accuracy.
3.3.2 Tag-based Learning Augmentation
In addition to the tag-aware encoders, augmented guidance through various training strategies. Similar to (5. we propose contrastive learning objectives on the tag space from both the user's perspective and the item's perspective:
also the tag and logic information to provide
Ca((u)
Σ tTET
log P(t+lu)-
Σ
log(1 - P(t
T-ET
(6)
(1(1 =
Σ t+E
log P(t+ii)-
Σ
log(1 - P(t
T-ET,
where P(t|u) Sigmoid (T.)) estimates the probability of user identified with a user role t, and P(( = Sigmoid(x (x] e,) estimates the probability of an item being related to user role I. In practice, can reuse 0 in section to realize the latter model P((]i). In the user level objective, To are user tags related to ground truth target items in Iy, and T, are tags related to sampled negative items. In the item level objective, T,t are user tags related to item i, and T are tags sampled from T\T. T.
Tag-Logic Exploration: For the settings of Tut,, To and T, we offer two alternatives that either use the original tag sets (0) = [t]t E arg, (Pt)k( (or T:(0) [ttlt E arg, (op-k(((t(())
for T that address the recommendation utility (denoted as TagCF-util) or use the extended tag sets Tu (1) (or Ti()) inferred by the logic graphs that address the interest exploration (denoted as TagCF -expp). For instance, we have target item that has an initial tag = "Symphonist" which is logically related to the topic e "Music Theory" according to GU2L_ Then using gi2u. we might explore and find that there exists a logic of "Music Theory" + "Teacher", where "Teacher" becomes the extended tag of the item. We provide detailed deseription of the general procedure in Appendix A.6 and the confirmatory case study in Appendix C.3
Augmented Learning: In summary, the augmented learning objective linearly combines the main objective with the two contrastive losses:
C(u, it ;i )= = Culu, ((,,it,i)))) + X 1 ( Cu(u) + Ln(it))
The resulting framework will align the item and user embedding space with the tag embedding space with À > O, which guides the model to match users and items according to the user tags.

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3.3.3 Tag-logic Inference Extension
Despite the implicit tag modeling through learning augmentation, we also provide an explicit tag-logic inference strategy further enhance recommendation performance and explainability. Specifically, we start from the user encoding @.. from Eq.(3) ( and find the initial user tags of user (0). Similar to logical exploration process section 3.3.2 we derive the extended tag set (1) according to gual* and GI Then, for each candidate item 2, we can use the obtained user tags to calculate the tag-based matching score:
Vtg (u, 1,
Σ P(41)) Vigg(u, (ET.
Σ
P(I))
(8)
(TVT..)
where P(t and P(t]u) are the same as those in (q. Finally, the overall score with explicit tag-logic inference extension becomes:
ū(u, i) = Draw (u, i) 9otun (u, i, 0) + 3r (u, 2,
where the Utility-based TagCF-util set Bo > 31 0, and the Exploration- TagCF-expl 8o 2 0, 31 > 0.
4
Experiments
4.1
Online A/B Test
4.1.1
Workflow Specification
We conduct an online A/B test on real-world industrial video recommendation platform eval- uate the effectiveness of TagCF-ut. The platform serves videos for over half billion users daily, and the item pool contains tens of millions of videos. Figure 3 provides a detailed overview of the implementation of our online recommenda- tion workflow. The tag extraction module, the collaborative logic reasoning module, and the training of all augmented models are offline pro- cedures executed
Recommendation
P
Pag
Rerank
Anguucated Scoring Madel
Lego Extention
Ranking
Retrieval
User hittoy Cantidare tiems
daily basis. In contrast, the inference part of the tag-logic integration mod- ule is deployed in the last ranking stage (which chooses top-6 scored items as recommendation from 120 candidates from the previous stage) for real-time recommendation requests, with prepro- cessed tag and logic information retrieved from the latest knowledge base. As we have described in Section 3.1 and Section 3.2 we use the cover set solution to achieve stable and efficient infer- ence that fulfills the industrial demand.
Oalinne Rec
Candidace Pools
T
C
C
Offline Processes
LLM--based Collaborative Logic Reasnning
New
MLLM based Tag Extraction
Videos
Figure 3: The deployment of TagCF in the online recommender system.
4.1.2 Evaluation Protocol
For our online experiments, we randomly assign all users into 8 buckets, each accounting for relatively 1/8 of the total traffic, with each bucket consisting of tens of millions of users. We deploy TagCF-util and TFgCF- in two different buckets and use two other buckets with the baseline model as comparisons. The baseline method (details omitted) in remaining buckets is state-of-the-art ranking system that has been developed for four years from (29]. To ensure the reliability and validity of the experimental results, each method is subjected an online testing phase of least 14 days. To evaluate recommendation accuracy, we focus on the key interaction reward that combines positive user feedback (e.g., effective play, like, follow, comment, collect, and forward). the novelty-based diversity metric [ that estimates the likelihood of recommending new video categories to a user, where the categories are predefined by human experts instead of the item tags in framework to ensure fair comparison.
also include
7

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Table 1: Online performances of TagCF and denotes the results are statistically significant.
Strategies
#Interaction
Diversity
TagCF wtil v.s. baseline
+0.946%
+0.001%
TagCF-exp v.s. baseline
+0.143%
+0.102%%
4.1.3 Effectiveness of Tag-Logic Augmentation in Practice
We summarize the results Table T which shows that both TagCF-util and TexCF- outperform the baseline but exhibit different behaviors: TagCF-util significantly improves interaction metries, which proves that the extracted tags can effectively represent the matching reasons and enhance recommendation accuracy. On the other hand, TagCF-expl significantly improves the diversity metric without losing recommendation accuracy, which proves that TagCF-ut can accurately explore user preferences through the logic graphs, mitigating the echo chamber effect. Moreover, conducted an extended experiment for TagCF-expl, increasing the traffic to buckets, and observed 40 days to validate the long-term effect. In addition to the improvement on the short-term diversity metric, we also observed a quantitatively and statistically significant boost of LT7 (a key metric that indicates long-term daily active users (DAU) and user refention benefits online in the next week) by 0.037%, proving the stable and consistent improvement on user satisfaction in the long run.
4.2 Offline Experiments
4.2.1 Experimental Setup
Datasets:
To further investigate the design choices of TägCF, we include two public datasets [39]. Books and Movies, as well as an offline dataset from our real-world industrial video sharing platform (i.e., Industry). More details about the datasets and preprocessing can be seen in Appendix B.2
We include common ranking accuracy indicators such as NDCG@ N and MRR @ N, well diversity metrics like ItemCoverage @ N and Ginilndex @ N (denoted as Cover@ and Gini @ N, respectively). In this paper, we observe N E {10, 20}. For each experiment training and evaluation for five rounds with different random seeds and
Evaluation Protocol:
across all models, report the average performance.
Baselines: We include BPR 43 as the standard collaborative filtering method, and include several representative seguential models, namely GRU4Rec (25), Bert4Ree [48]. SASRee [29]. LRURec [59]
Mamba4Rec [34]. We also compare with LLM-enhanced recommender approaches: RLM 42
SAID 27 and GENRE 337. See more baseline details in Appendix B We follow RecBole
66
as the implementation backbone and reproduce all baselines with hyper-parameters from either the
original setting provided by authors or fine-tuning using validation.
4.2.2 Effectiveness of LogiL Integration
We present the overall experimental results in Table 2 Compared BPR and sequential models, RLMRec and GENRE generally consistently improve the accuracy metric and, in most cases, improve the diversity, which are the best baseline methods. However, we methods is not always statistically significant, especially in datasets with large scale (e.g., Industry). Additionally, the BPR model outperforms other methods in the diversity metric by large margin, but this comes with severe sacrifice in recommendation accuracy. Excluding this exceptional model, our proposed Tit and TagCF-ut consistently outperform all other baselines in accuracy and diversity metrics, providing extended verification for the expressiveness of the extracted tags and logic graphs, as well as the effectiveness of the tag-logic integration framework.
that the improvement of these
4.2.3 Transferability Test
To validate that the extracted tags (i.e., T and C*) and logic graphs (i.e.,gtt++ and Gl2U+) in our industrial solution encapsulate general knowledge, we conduct a cross-task transfer experiment. Specifically, we use the same tag extraction module in [q. to generate the data-specific tags for Books and Movies data. Then we use the semantic embedding [10] of these tags to find the closest tags in T and C* so that the tag space is completely aligned. This means that the TagCF solutions

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Table 2: Overall performance comparison lower better. The best performance is denoted in bold and the second is underlined (excluding the exceptional trade-off behavior of BPR in Books dataset). t-test with p-value 0.005 and "Improv." denotes the improvements over the best baselines.
offline Industry dataset and public datasets.
Datasct
Mothod
NDCG@10
NDCG@20
MRR @
MRR@20
Coverê 10
Cover@20
Gini@ 101
Gini 201
MF-PPR
0.0145
0.0215
0.0124
0.0147
0.1140
0.1682
0.9814
0.9720
GRU4Rec
0.0177
0.0253
0.0118
0.0137
0.2364
0.3514
0.9656
0.9815
SASRec
0.0182
0.0257
0.0121
0.0140
0.2704
0.3790
0.9617
0.9452
Bert4Rce
0.0165
0.0232
0.0109
0.0125
0.256
0.3577
0,9700
0.9561
LRURee
0.0121
0.0143
04763
0,9532
0,9372
Industry
Mambu-iRee
0.0181
0.0253
0.0121
0.0142
0.3392
0.4489
0.9614
0.9452
RLMRoc
0.0256
0.0122
0.0141
0.9575
0.9421
0.0264
0.0145
0,9557
GENRE
0.0183
0.0262
0.0123
0.0142
0.3401
0.4602
0.9591
0.9417
TogCF-i
0.0198
0.0270
0.0134
0.0155
0.4013
0.5440
0,9316
0.9071
TogCF-ur
0
0.0276'
0.0132
0.0152
0.3833
0.5210
0,9370
0,9129
Improv.
+8.06%
+4.55%
+6.35%
+6.90%
+12.78%
+14.21%
+2.27%
+3.21%
MF-BPR
0.0633
0.0777
0.0481
0.0520
0.9636
0.9957
0.5511
0.5025
GRU4Rec
0.1449
0.1644
0.1
0.1214
0.6570
0.8116
0.7915
0,7558
SASRec
0.1597
0,1800
0.1241
0.1297
0.7968
0.8999
0,7790
0,7536
Bert4Ree
0.1060
0.7940
U.7612
0.1549
0.1
Books
Mambu4Rec
0.1641
0.1826
0.1330
0.1381
0.7970
0,9078
0.7767
0.7497
0.1661
0.7762
0,7695
GENRE
0.1674
0.1903
0.1332
0.1384
0.8213
0.9270
0.7749
0,7402
T-gCF-a
TagCF-ur
0
0.2071
0.1560
0.1613"
0.8438
0.9399
0.7469
0.7194
Improv.
+0.35
+7.86%
+13.60%
12,56%
12.40%
5..0%%
26.21%
10%5
MF-BPR
0.0574
0.0695
0.0432
0.0465
0.7692
0.8887
0.8170
0.7971
GRU4Rec
0.1181
0.1275
0.1058
0.1083
0.6565
0.7977
0.8319
U U.8060
SASRec
0.1171
0.1271
0,1018
0. 1045
0. 0.8472
0.9183
0.7960
0.7867
Bert4Ree
0.1118
0.1216
0.0994
0. 1020
0. 0.7925
0.9012
0.8331
0.8128
LRURee
0.1201
0.1307
0.1051
1080
0.8786
0.9452
0,7746
0,7648
Movies
Mamba4Rec
0.1193
0.1072
RLM
0.1192
0.1304
0.1049
0.1076
0.8381
0.8912
0.7913
0,7738
SAID
0.1210
0.1311
0.1057
1082
0.8397
0,8956
0,7975
0,7804
0.7715
0.7601
TogCF-i
0.8956
0.9575
0.7173
TogCF-ut
0.1258
0.1346
0.1134
0.1159
0.8813
0 0.9540
0,7668
0,7490
Improv. +3.72%
+2.67%
+7.28%
+7.12%
+4.39%
+1.30%
+4.20%
+5.63%
for these two public datasets can skip the collaborative logic reasoning module and directly use GUZI+ ehperimeene
and gl2U+ for tag exploration. The experimental results are presented Table 2 and we observe that
TFgCF variants consistently demonstrate superior recommendation accuracy and diversity in Books and Movies, proving its transfcrability to other tasks. Note that other LLM based baselines also use the extracted tag-logic information to enhance recommendation in experiments, which indicates that the knowledge is transferable to other methods as well.
4.2.4 Ablation Study
TagCF
w/o TE
w/b TA
w/b TU
Industry
Book
0.20
0.0250
NDCog20
0.0225
0.18
0.12
0.0200
0.30
0.0150
0.12
TagCF-it
TayCF-ut
TagCF-it
TogCF-ut
TagCF-t
TagEF-ut
Figure 4: The ablation results of the three key methods of the tag-logic integration module.
Integration methods: To evaluate the individual impact of the three main components in the integration framework (section(.3) we compare full TagCF with three alternatives each disables one component in (tag-based encoder, tag-based learning augmentation, and tag-logic inference). denoted as w/o TE, w/o TA, and w/o TLII respectively. We show the results on the Industrial dataset in Figure 4 which verifies that all three components contribute to the recommendation accuracy.

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The same conclusion applies to diversity metrics as well and the results are illustrated in Figure g of Appendix [.2] The performance degradation observed in each ablated variant underscores the complementary value of each module within the integrated framework.
Effect of 3o and 31 We also analyses the impact of the inference scores of tags on recommendation performance. As shown in the figure 5) varied the values of different weights 80 and (1 to analyze the effects of the original Utility-based tag score and Exploration-based tag score on the recommendation results.
Effect of X: We alter the À in Eq. and present the results in Figure 6 We can see that there exists an optimal point in the middle, indicating the effectiveness of the learning augmentation.
Effect of k: We conduct experiments with different number of tags extracted for each item (A: E {20, 50, 100, 200, full)) and present the results in Figure 9 in Appendix B.2
NDCG@10(industr))
NDCG@10(Mevies)
NDCG@10lBooks)
0.1
0.07
0.00
0.18-4
40:7
4.18
0.38
0.05
00.10
4.34
4.15
0.07
0.05
0.30
0.00
0.00
0.0
1e-1
0.005
1c-2
0.06
0.02
0.03
0.30
0.24
-0.30
< 1c-2
0.05
0.01
0.07
0.30
0.37
1c-2
0.11
0.29
0.03
0.08
0.36
0.0
16-3
0.32
0.09
04
0.35 4.15
-0.15
1e-3
0.34 2.05 0.11 0.30
4.10
1e-3
0.00
0.15
0.07
0.00
4.39
-02
0.20
--0.2
0.02
6.21
0.34
0.12
10.3
16.3
16-2
16-1
1o.3
16-2 16-1
81
81
81
Figure 5: The model performance with different 3p and 31.
MRR
NDCG@10
MRRg520
NDCGg20
Industry[bagF-itt
IndustrylTbgtF-ug
Perfomcncc
0.024
0.024
0.020
0.020
0.012
0.012
0.01
0.05
0.1
0.2
0.5
0.01
0.05
0.1
0.2
0.5
Figure 6: The model performance under different A.
4.2.5 User Tag vs. Item Tag
In Table 2 we find that TagCF-ut tends to yield greater improvements in accuracy metrics, indicating that the user tag set is potentially more effective and stable in capturing preferences and personalities of users, solving the role identification task. This phenomenon might be related to the fact that user role tags are likely to be stable concepts with better expressiveness, which can be partially explained by the smaller cover set size compared with item tags shown in Table of Appendix C.I In contrast, item tags may have a shorter lifespan (e.g., a special topic in recent news) and may frequently update even in the cover set. This may also explain the optimal diversity performance of TFgCF-it, since the more fine-grained item tag set can contribute more diverse options during training and inference.
5 Conclusion
In this work, we emphasize the importance of the modeling of users' roles and the user-item behavior logic in the semantic tag space, and propose new recommendation paradigm, TagCF, that can effectively extract item/user tags from items with MLLM, infer realistic behavioral logic of users with LLM, and enhance recommendation performance with the tag-logic knowledge. We provide technical details of our efficient and effective solution of TagCF, which has been successfully deployed in our industrial aidoo-sharigg platform. We also verify that the extracted knowledge of the logic graph is a general transferable asset to other recommendation tasks and L.MM-aaed augmentation methods. Compared to the item tag set, the user role tags are empirically more stable and have more potential in improving recommendation accuracy, shedding light on an alternative design choice to the traditional item-tag-be based methodology, posing new challenges to recommender systems.
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References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Igee Akkaya, Florencia Lconi Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprini 0xX1v:2333.8877, 2023.
[2] Gediminas Adomavicius and YoungOk Kwon. Improving aggregate recommendation diversity using lanking-based techniques. IEEE Trans. Knowl. Data Eng., 24(5):896 911, 2012.
[3] Deepak Agarwal and Bee-Chung Chen, fida: matrix factorization through latent dirichlet allocation. In Proceedings of the third ACM international conference on Web search and data mining, pages 91-100, 2010.
[4] Sajad Ahmadian, Milad Ahmadian, and Mahdi Jalili. A deep learning based trust-and tag-aware recom- mender system. Neurocomputing, 488:557-571, 2022.
[5] Alkiviadis G Akritas and Gennadi Malaschonok. Applications of singular-vale decomposition (svd). Mathematics and computers simulation, 67(1-2):15 31, 2004.
[6] Keqin Bao, Jizhi Zhang, Yang Zhang. Wenjie Wang, Fuli Feng, and Xiangnan Hc. Tallree: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems, pages 1007-1014, 2023.
[7] John S Breese. David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering, arXiv preprint arXiv:1301.7365 2013.
[8] Mu Cai, Jianwei Yang. Jianfeng Gao, and Yong Jae Lee. Matryoshka multimodal models. arXiv preprint M..iv:2405.174300 2024.
[9] Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. Lightgel: Simple yet effective graph contrastive learning for recommendation. arXiv preprint arXiv:2302.08191 2023.
[10] Jianly Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. Bge m3-emhedding: Multi-lingual, funttiiitntiiiiitt... multi granuagranullrity text embeddings through self- knowledge distillation. 2024.
[11] Junyi Chen, Lu Chi, Bingyue Peng, and Zehuan Yuan. Hllm: Enhancing sequential recommendations via hicrarchical large language models for item and user modeling. arXiv preprint arXiv:2409. 12740, 2024.
[12] Zhixuan Chu, Yan Wang, Qing Cui, Longfei Li, Wenqing Chen, Zhan Qin, and Kui Ren. Lim- guided multi-view hypergraph learning for human-centric explainable recommendation. arXiv preprint arXiv:2401.08211 2024.
[13] Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint Stt:::2005008084 2022.
[14] Jiaxin Deng, Shiyao Wang, Kuo Cai, Lejian Ren, Qigen Hu, Weifeng Ding, Qiang Luo, and Guorui Zhou. Onerec: Unifying retrieve and rank with generative recommender and iterative preference alignment. arxiv preprint ar Xiv:2500 18960, 2025.
[15] Sahraoui Dhelim, I. Liming Chen, Nyothiri Aung, Wenyin Zhang, and Huansheng Ning. A hybrid personality- aware recommendation system based on personality traits and types models. Journal of Ambient Intelligence and Humanized Computing, 1479:12775--27888 2023.
[16] Sahraoui Dhelim, Huansheng Ning, and Nyothiri Aung. Compath: User interest mining in heterogencous signed social networks for internet of people. IEEE Internet of Things Journal, 8(8):704470355, 2020.
[17] Yunfan Gao, Tao Sheng, Youlin Xiang. Yun Xiong, Haofen Wang, and Jiawei Zhang. Chat-rec: Towards interactive and explainable llms-augmented recommender system. arXiv preprint arXiv:2303.1 145224 2023.
[18] Yinggiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, and Yongfeng Zhang. A survey on trustworthy recommender systems. ACM Trans. Recomm. Syst. 3(2), November 2024.
[19] Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, and Yongteng Zhang. Un- derstanding echo chambers in e- e-commerce recommender systems. In Proceedings of the 43rd international ACM SIGIR conference research and development information retrieval, pages 2261 2261-2270, 2020.
11

---

[20] Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems, pages 299-315, 2022.
[21] Lewis R Goldberg. An alternative "description of personality": The hig-five factor structure. In Personality and personality disorders, pages 34 47. Routledge, 2013.
[22] Manish Gupta, Rui Li, Zhijun Yin, and Jiawei Han. Survey on social tagging techniques. ACM Sigkdd Explorations Newsletter, 12(1)258-72, 2010.
[23] Ruining He and Julian McAuley. Fusing similarity models with markov chains sparse sequential recommendation. In 20/6 IEEE 16th international conference on data mining (ICDM), pages 191-200. IEEE, 2016.
[24] Xiangnan He, Kuan Deng. Xiang Wang. Yan Li, Yongdong Zhang, and Meng Wang. Lightgen: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development Information Retrieval, pages 639-648, 2020.
[25] B Hidasi.
Session-based recommendations with recurrent neural networks.
arXiv preprint
arXiv: 0669999 2015.
[26] David M. Howcroft, Anya Belz, Miruna Clinciu, Dimitra Gkatzia, Sadid A. Hasan, Saad Mahamood, Simon Mille, Emiel van Miltenburg. Sashank Santhanam, and Verena Rieser. Twenty years of confusion in human evaluation: Nlg needs evaluation sheets and standardised definitions. In Brian Davis. Yvette Graham, John Kelleher, and Yaji Sripada, editors, Proceedings of the 13th International Conference on Natural Language Generation, pages 169-182. Association for Computational Linguistics, December 2020, Funding Information: Howeroft and Rieser's 's contributions were supported under EPSRC project MaDrIgAL (EP/NO17536/1), Gkatzia's 's contribution was supported under the EPSRC project CiViL (EP/T014598/1) Mille's s contribution was supported by the European Commission under the H2020 contracts 870930-RIA, 779962-RIA, 825079-RIA, 786731-RIA. Publisher Copyright: @ 2020 Association for Computational Linguistics; 13th International Conference on Natural Language Generation 2020, INLG 2020 Conference date: 15- 12-2020 Through 18- 12-2020.
[27] Jun Hu, Wenwen Xia, Xiaolu Zhang, Chilin Fu, Weichang Wu, Zhaoxin Huan, Ang Li, Zuoli Tang, and Jun Zhou. Enhancing sequential recommendation via Ilm-based semantic embedding learning. In Companion Proceedings of the ACM Web Conference 2024, pages 103-111, 2024.
[28] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint 240.22241. 2024.
[29] Wang- Cheng Kang and Julian McAuley, Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM), pages 197-206. IEEE, 2018
[30] Amar Khelloufi, Huansheng Ning. Sahraoui Dhelim, Tie Qiu, Jianhua Ma, Runhe Huang, and Luigi Atzori. A rocial-rclationnsii--brree service recommendation system for siot devices. IEEE Internet of Things Journal, 8(3): (43)11859-188 1870,870, 2020.
[31] Chen Li, Yixiao Ge, Jiayong Mao, Dian Li, and Ying Shan. Taggpt: Large language models are zero-shot multimodal taggers. arXiv preprint 2rXiv:2304.03022. 2023.
[32] Jinming Li, Wentao Zhang, Tian Wang, Guanglei Xiong, Alan Lu, and Gerard Medioni. Gpt4rec: A generative framework for personalized recommendation and user interests interpretation. arXiv preprint arXiv:2304.00 008899 2023.
[33] Greg Linden, Brent Smith, and Jeremy York. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1):66-80 80,6-800220 2003.
[34] Chengkai Liu, Jianghao Lin, Jianling Wang, Hanzhou Liu, and James Caverlee. Mamba4rec: Towards efficient sequential recommendation with selective state space models. arXiv preprint arXiv:2403.0390 2024.
[35] Chenxi Liu, Yonggiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, and Kun Zhang. Discovery of the hidden world with large language models. arXiv preprint arXiv:2402.03941 2024.
[36] Fan Liu, Yaqi Liu, Huilin Chen, Zhiyong Cheng, Liqiang Nie, and Mohan Kankanhalli. Understanding before recommendation: Semantic aspect-aware review exploitation via large language models. ACM Transactions on Information Systems, 2023.
12

---

[37] Qijiong Liu. Nuo Chen, Tetsuya Sakai, and Xiao-Ming Wu. Once: Boosting content-based recommendation with both open-and closed-sous source large language models. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pages 452-461, 2024.
[38] Andriy Mnih and Russ R Salakhutdinov. Prohabilistic matrix factorization, Advances in neural information processing systems, 20, 2007.
[39] Jianmo Ni, Jiacheng Li, and Julian McAuley. Justifying recommendations using distanly- reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLA pages 188 197, 2019.
[40] OpenAL. Emheddings: What are embeddings?, 2025.
[41] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang. Sandhini Agarwal, Katarina Slama, Alex Ray, al. Training language models to follow instructions with human feedback. Advances neural information processing systems, 35:27730- 27744, 2022.
[42] Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. Representation learning with large language models for recommendation. In Proceedings of the ACM Web Conference 2024, pages 3464-3475, 2024.
[43] Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv: 2205.2618, 2012.
[44] Badrul Sarwar, George Karypis. Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285-295, 2001.
[45] Fu Shang, Fanyi 7hao, Mingxuan Zhang, Jun Sun, and Jiatu Shi. Personalized recommendation systems powered by large language models: Integrating semantic understanding and user preferences. International Journal of Innovative Research in Engineering and Management, 11(4)399-49 2024.
[46] Yi- Di-Dong Shen, Zhong Zhang, and Qiang Yang. Objective-oriented utility- association mining. In 2002 IEEE International Conference on Data Mining, 2002. Proceedings. pages 426 433. IEEE, 2002.
[47] Peter Spirtes, Clark Glymour, Richard Scheines, and Rohert Tillman. Automated search for causal relations: Theory and practice. Heuristics, Probability and Causality: A Tribute to Judea Pearl, 2010.
[48] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management, pages 1441-1450, 2019.
[49] Matthew J Vowels, Necati Cihan Camgoz, and Richard Bowden. D ya like dags? survey on structure learning and causal discovery. ACM Computing Surveys, 55(4):1-36, 2022. 2022..
[50] Wenjie Wang. Yang Zhang, Haoxuan Li, Peng Wu, Fuli Feng, and Xiangnan He. Causal recommendation: Progresses and future directions. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 3432-3435, 2023.
[51] Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, pages 165-174, 2019.
[52] Yan Wang, Zhixuan Chu, Xin Ouyang, Simeng Wang. Hongyan Hao, Yue Shen, Jinjie Gu, Siqiao Xue, James Zhang, Qing Cui, et al. Llmrg: Improving recommendations through large language model reasoning graphs. Proceedings of the AAAI Conference Artificial Intelligence, volume 38, pages 19189-19196, 2024.
[53] Jason Wei, Xuezhi Wang. Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, et al. ofain-o--thougll prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824 24837, 2022.
[54] Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. Next-gpt: Any-to-any multimodal 11m. arXiv preprint arXiv:2309.00 053999 2023.
[55] Hong- Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. Deep matrix factorization models for recommender systems. In IJCAI, volume 17, pages 3203 3209. Melbourne, Australia, 2017.
13

---

[56] An Yang, Baosong Yang. Beichen Zhang, Binyuan Hui, Bo Zheng. Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang. Haoran Wei, et al. Qwen2. technical report. arXiv preprini arXi:22412.5515,, 2024. 2024.
[57] Fan Yang, Zheng Chen, Ziyan Jiang, Funah Cho, Xiaojiang Huang, and Yanbin Lu. Palr: Personalization Ilms for recommendation. arXiv preprint arXiv:2305.07622. 2023.
[58] Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoe Viet Hung. and Hongzhi Yin. Xsimgel: To- wards extremely simple graph contrastive learning for recommendation. IEEE Transactions Knowledge and Data Engineering. 36(2):913 926, 2023.
[59] Zhenrui Yue, Yueqi Wang. Zhankui He, Huimin Zeng. Julian McAuley, and Dong Wang. Linear recurrent units for sequential recommendation. Proceedings of 17th ACM International Conference on Web Search and Data Mining, pages 930-938, 2024.
[60] Dan Zhang. Yangliao Geng, Wenwen Gong, Zhongang Qi. Zhiyu Chen, Xing Tang, Ying Shan, Yuxiao Dong, and Jie Tang. Recdel: Dual contrastive learning for recommendation. In Proceedings of the ACM on Web Conference 2024, pages 3655-3666, 2024.
[61] Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, Xiaofang Zhou, et al. Feature-level deeper self-attention network for sequential recommendation, In IJCAI, pages 4320 4326, 2019.
[62] Qian Zhao, Hao Qian, Ziqi Liu, Gong- Duo Zhang, and Lihong Gu. Breaking the barrier: utilizing large language models for industrial recommendation systems through inferential knowledge graph. In Proceedings the 33rd ACM International Conference on Information and Knowledge Management, pages 1086-5093, 2024.
[63] Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan. Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, et al. Recbole: Towards unified, comprehensive and efficient framework for recommendation algorithms. In proceedings of the 30th acm international conference on information & knowledge management, pages 4653-4664, 2021.
[64] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Be- ichen Zhang, Junjie Zhang, Zican Dong, al. A survey of large language models. arXiv preprint arXiv:2330.. 18223, 2023.
[65] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging ilm-as-a-judge with mi-bench and chatbot arena. Advances Neural Information Processing Systems, 36:46595-46625 2023.
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for what
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A TagCF Specification
A.1 Task Formulation
Top-N Recommendation Task Define the set of users U and the of items I. The observed user-item interactions are represented as user histories H. where each user's history H. = has length Ru. For top--V recommenaation, the objective is to learn a scoring function P(ilu) that suggests top-N items (from I \ H.)) that each user u is highly likely to engage with, and the ground truth positive target item set is denoted as In- Following the collaborative filtering paradigm, we assume binary interaction label Ve,i, indicating the user u's positive feedback an item i, and we also allow an additional reward weight signal w;+ for each positive item it I.. to accommodate multi-behavie modeling which is related to the tag- bag-based encoder in our solution. Yet, we remind readers that there may exist other context features that include but are not limited to user profile features, time features, device, and network features. How these features may integrate the tag-logic information is out of the scope of this paper, but worth further investigation.
scenarios. In terms the model input, we focus on the user history
User/Item Tag Identification Task As we have introduced in Section 3.1 denotes the set of all possible item (topic) tags (e.g. headphone) and T denotes the set of all possible user (rolc) tags (c.g., symphonist). We assume that neither C nor T are known in advance, so we need an automatic inference framework to solve them. Recall that one of our focuses in this work is the user role identification task which formally finds a subset of user tags it is impractical directly ask users to provide
To C T that describes given user As discussed in Section D this information, but we can solve it by first figuring the user tags T. C T related to each item i, then learn a tag-based model to infer T... This complements the conventional viewpoint that first associates the item topic tags C, C C to each item i, then predicts the item tag user profile C. C.
Behavioral Logic Reasoning Task With the discovered item tag set C. and user tag set T. we then solve the logical connections between them. Specifically, we aim to find mapping E T x C - [0. 1] that estimates the probability P(c]t) of certain U2T logic (i.e., a symphonist likes violin), as well as mapping elu C x T + [0. 1] that estimates the probability P(t[c) of a certain I2U logic (i.c.. headphone is beneficial to a symphonist). These two mapping functions semantically define the edges of directed logic graph between C and T, and we denote the corresponding sub-graphs as GUa = (V, ( and glau (V, 22), where V = -TCC. As a practical assumption, we do NOT assume that the two tag sets are mutually exclusive, i.e, TnC # 0. since a user role might he considered as a topic as well (e.g. video about symphonist). Same as the tag identification task. there is no ground truth label in this task, and we will take advantage of the generation and reasoning ability of LLMs approximate the actual behavior logic.
A.2 Notations and Terminologies
We summarize the key notations used in this paper in Table Additionally, we find that "topic" and "interest" are two semantically confusing terms that both express the item type tags. In our paper, we refer to "topic" as the item type in the view of an item (i.e.. P(cli)) and "interest" as the item type in the view of a user (i.e., P(clu)).
A.3 Prompt Designs
We provide the prompt design details for the tag extraction task in Section 3.1 and present examples of the MLLM response in Table[4] The input textual features of [TiRe/[ASR/[OC]] are preprocessed text from the
MLLM that describes the contents of the item, and we remind readers that this design might be task specific (e.g., Books datasets only uses [TTitle)).
Then, we provide the prompt design details for the collaborative logic filtering module in Section B.2 and present examples of the LLM's output in Figure 7 In practice, we find that including an intuitive example with input and output significantly improves the interpretability and the recognition rate of tags during post- post-processing.
A.4 Tag Extraction Algorithm
In section 3.1
we introduce the process of cover set reduction which aims find small subset of the full tag set that can cover a sufficient number of items while ensuring the semantic differences between tags. We present the algorithm in Alg and the process runs on daily basis. The process itcratively includes new tag into the cover set, and each newly included tag maximizes the coverage on the uncovered items (line 6) until no less than = 99% of the items has at least one tag included in the cover set. Tags that have not been recalled by any item the last ID days will be removed (line 11), indicating an out-of-date tag. In practice, we observe that the cover set converges (less than 10 tag removed or added per day) after 30 days of updates. The statistics of the resulting tag sets and their cover sets are summarized in Table Appendix C
As we have discussed in Section 3.1
we assume a computational bottleneck during inference of Eq. which indicates that the system can only support the MLLM inference subset I' C I (around 500,000 items per
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Table 3: Key Notations
Symbol
Description
U,I
set of users and items specific user and item interaction history of user u positive target items of user
u. i Hy
Iu
T,
set of user role tags and item topic tags the extracted cover sets in section 3.1
T C*
Ta, Ti
user role tags inferred for user u. and item correspondingly item topic tags inferred for user u and item i, correspondingly the initial inferred tag sets from user
C.. C.
(O) G GiU
the logic graphs extracted on the full tag sets the logic graphs extracted on the cover sets the edge mappings for logic graphs
guzl g
E e
Po(t))), Po(cl) P.(ttc) P(ll),P(ilH P(Iw), P(c'u) P(J)), P(cli)
The distilled model for tag extraction in Section 3.1
the distilled model for U2I and I2U logic prediction in Section 3.2 the inferred likelihood of engagement for the user-item pair user role and item interest prediction of user u user role and item topic prediction of item i
e,, Cc
tag embedding of a specific user role and item topic the sets of tag embeddings related to an item item embedding inferred by tag- based encoder ID-based item embedding
Tr C
(t) (e)
X;
Fu: Xa Dx
user embedding inferred by tag embedding and ID embedding sequence final user embedding from the user encoder
Table 4: The prompt templates for item tag identification and user role tag identification
Item Tag Extraction Prompt Template
MLLM Response Example
This video's [Tide]] [[SR]/ / [[CR] information. To the video interesting for users, please extract 8-10 independent and detailed interest tags based on the multimodal contents.
[Pet Videos; Family Warmth; Song Cover Challenge: Pet Companionshin: Music Production; Newhom Puppy: Cute Style; Daily Life]
User Tag Extraction Prompt Template
MILM Response Example
This is the video's [Tidee/ / [ASR]/ / (OCR] information. Identify 8-10 distinct targel andience segments that would find this video appealing.
[Fashion Enthusiast; Beauty Influencor; Fashion Designer; Personal Image Consullant: I lairstylist; Fashion Critie;; Inter- Celebrity. Fashion Photographer!
as family." "xx professionals." "xx enthusiasts."
day). and we learn distilled model / to solve the tag extraction problem for the remaining items in \ I'. Without textual generation, we find that the multi-modal embedding model [00] is sufficiently efficient to infer all newly uploaded items each day, and it reasonable to believe that the output E, contains sufficient information of the item to accurately infer the corresponding tags. Thus, we adopt Pa(tl) = (t E.) and Po(cl)) = Po E.).
A.5 Logic Reasoning Process
In Section 3.2 we have introduced the collaborative logic filtering task and proposed to infer the logic graph in
the cover set with distilled models. Specifically, when inferring logically related tags for given source tag using Eq.2), the output tags may or may not appear in the cover set due to the unrestricted open world generation. In practice, we find that the generated tags rarely match those tags in the cover set, but is likely to find semantically close alternatives. Thus, we train distilled models (c(ctt) : T x C -> [0. 1] and (t]c) : C x T - [0. 1] based on the offline data generated by 2) each day. The models take the semantic embedding of tags input and output the likelihood of logical connection between the (full) sets C and T. After the daily training, we use (clt) to predict scores of e C* with the given source tag E T and use Pe (t(cc to predict scores of t E T with the given source tag c E C*. Empirically, we observe that the top-50 predicted tags are semantically accurate logical connections in most cases, and the top-20 predicted tags are sufficiently diverse. Thus, we adopt the top-20 connections edges in gUll+ and glU.
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U21 Reasoning Instruction
12U Reasoning Instruction
List Video Viewing Preferences Based User
List Potential Audience for Video Type:
Example:
Esample:
Input:
The user is (parenti
Inpot:
The content (education).
Output:
Ourput:
1. Educational videos
Cooking
2. Educalors
Health finness
3.
Toch enthusinat
Parent child reading
8. SelE-earners
Now, list video vien preferences
following user:
Now, list potential audience for the following video: (item Lug)-
Input: The user is [user_tan tagl-
Inpul: The vidco
Output:
Ouiput:
U2I Reasoning Output
Reasoning Output
Input: user lyoung person).
The videa has abour (coaking tutorial).
Output:
Output:
Music videos and concerts
L.Cokking entiusiasts
Dance fttress
2.Homemakers
Fashioo and makoup tutorials
3.Food bloggers
8.Ani
Healthy cating
(a) U2I Reasoning Instruction and LLM Output
(b) I2U Reasoning Instruction and LLM Output
Figure 7: The instructions for collaborative logic reasoning.
Algorithm 1: Dynamic Cover Set Reduction Algorithm
Input: Most up-to-date inferred item-tag mapping M within cover set S; The day tag-item history of tags in cover S
set S = T (or S C* in TagCF-it) (set if not exist); Newly
that have been covered by S and report tag recall rate; 2: Stetect find all tags in S that have covered items in M;
1: Teowerod +- find all items
3: Inew <- find all items appeared in M;
4: Snew find all tags appeared in M;
5: while /eoveredl/ [nnow| < T do
6: Find the tag t Snow S that covers the most number of items in Inew Teovered:
7:
This ensures semantic differences between selected tags in S 8: Sselect f- Sselect (t) and update Iowered with newly covered items;
9: end while
Append new day history H with data M and remove the history in the oldest date;
11: Remove tags in Stelec that have no records in H:
12: Store updated cover set T + Sselect (or C* <- S) and the updated tag-item history H.
Different from sets that quickly converge in size, the full tag continuously expand themselves and the same happens to corresponding logie graphs. Although one can assume the possible tags the open world are limited and expect the graphs to converge eventually, we notice that the 30-day inference already generates a graph too large to be directly used under the latency requirement. Additionally, the majority of the tags in the full set well as their corresponding logic connections usually fine- fine-grained with very strong interpretability. but only cover small of items or user behaviors with undesirable generalizability. In general, we believe that the cover set tag-logic better suits the statistical models the recommender systems, while the full set tag-logic a better choice for detailed explanation.
A.6 Augmentation Model Specification
Details of Tag-based Encoders: Define the user tag embedding sequence as T; (et, etg..... ] E d where each tag is associated with a learnable d-dimensional vector. Similarly, define the item tag embedding
sequence as C, (eey C ecy] E R dd Then, we calculate the tag- based item encoding r. e Rd and (c) E IR:d by fusing the tag embeddings using item encoders:
r = (T.,), 9(C.).
(10)
The fusion functions f and g can be accomplished through methods such as the Mean Pooling or Attention Mechanism (61). We adopt the latter attention mechanism in practice to model the different importance of each tag and the mutual influences between tags. Specifically, taking role tags (in TagCF-ur example, the

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adopted Attention operation is formulated as:
oo:T; T,, a; softmax (WT, + b)
(11)
where W E R and b E R are learnable parameter weights. a; is the tag attention score. This formulation enables the model to prioritize informative tags while suppressing noise, enhancing the discriminative power of the resulting tag- based item representation r!"".
Then for given user and the corresponding history Hr (f1,- in J. first obtain the standard ID- based item embedding sequence X., = |xi...... xi,] E IRdx and the tag-based item embedding sequence R = (+))) x E _dxx each obtained from the tag- based item encoder, i.e. Eq. (0). We then include
two SASRéc-style (29) user encoder networks with identical architecture which first obtain separate hidden embeddings of the user:
Pu IremSASReoXX.), TagSASRec(R!").
(12)
where Pu E He and r E R (TagCF-it outputs r(c) 12d instead). Note that one can also try other user encoding schemes such as item embedding concatenation or addition followed by a single encoder, but empirically, we find that the separate encoder networks yield the best results.
Log-Logie Exploration in Learning and Inference Augmentation: Without loss of generality, we explain the exploration strategy user role tags in TagCF-ut -ut as the extended description of Section [.3.2] and B.3.3
and the solution in TFg symmetric. Wc start by considering the initial tag set (()) that focuses on improving utility (i.e. TagCF-util) where T(0) may represent T:(0) for a given item (inferred from P((()) or Tw, for a given user (inferred from P() Then we can use the U2I logic graph to find logically related item topic tags as C(1) fc|Bt T(0), s.t. (t,c) euil.) }. Note that we use the distilled model to generate graphs, and the corresponding scores could be used as weights of the edges. In this case, it can also use a soft method that selects the tags with aggregated weights as C(1) = {clwe > 63. where We = Exe (t.a))) P(c]t). Finally, we can obtain the final exploration lag set T(1) by applying I2U logic on C(1) T(1)= {4Ee E C(1), (c,t) g }. Again, the corresponding soft method gets T(1) = (1/,,, > 83. where w. tet) We. To better align the scale of weights in T(0) and T(1), we normalize the weights that they sum up to one. For better illustration of this process, we further provide case studies of the difference hetween T(0) and T(1) in Appendix C33
Note that with an average hranch factor b, we would observe [T(1)] = (b**)), which is several magnitudes larger than the initial set, so we truncate the top-k tags in T(1) according to the frequency or weights to reduce noise. resulting in [(1)] [O) In practice, we can achieve fast computation of these processes by representing the graphs sparse adjacency matrices and engaging multiplication with parallel computing.
B Experimental Settings
B.1 Online Experiments
Implementation Details. We conduct online A/B test real-world industrial video recommendation platform to evaluate the effectiveness our method. The platform serves videos for over half billion users daily, and the item pool contains tens of millions of videos. The number of candidates for each request in this stage is 120 and the videos with top-6 scores are recommended to users. To ensure that tag encompasses over 90% of user video views, process 3 million videos daily by tag extraction modulc deployed on cluster of 50 NVIDIA 4090 GPUs
Evaluation Protocol. For our online experiments, we randomly assign all users into buckets, each accounting for relatively 1/8 of the total traffic, with each bucket consisting of tens of millions users. We deploy TagCF- wtil and TagCF-expl in two distinct buckets, while reserving two additional buckets for the baseline model comparison. The remaining buckets employ state-of-the-art ranking system (details omitted for brevity) that has been iteratively optimized over four years 29 To ensure statistical reliability, each experimental condition undergoes minimum 14- day online testing phase. evaluate recommendation accuracy, we focus on the key interaction reward that combines positive user feedback (e.g., effective play, like, follow, comment, collect, and forward). We also include the novelty-based diversity metric (2) that estimates the likelihood of recommending new video categories to user, where the categories are predefined by human experts instead of the item tags in our framework ensure fair comparison.
B.2 Offfine Experiments
Datasets. We include two public datasets (39 Books and Movies, as well as an offline dataset from real- woaldworld industrial video sharing platform (i.e., Industry). For public datasets, utilize product descriptions textual
25

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layers SASRec with hidden size Table 5: The statistics of the datasets.
Dataset
#Users
#Items
#Interactions
#Sparsity
Books
9,209
8,299
935,958
98.77%
Movies
39,832
24,050
1,103,918
99.88%
Industrial
89,417
10,396
3,292,898
99.64%
features and filter out products without descriptions. We convert the ratings of or larger positive interactions. For the Industrial dataset, first select around 10k photos and obtain audio, visual, and textual features of each video. Then take the interactions these photos in day the training and those in the next day as test set (excluding unseen users). To ensure quality of the dataset, we follow common practice [48] 23 29] and keep users with at least ten interactions through n-core filtering. The statistics of the processed datasets are summarized in Table
Evaluation Protocol. We include common ranking accuracy indicators such as NDCG@ N and MRR @ N, well diversity metrics like ItemCoverage @ K and Ginilndex @ (denoted as Cover @ N and Gini @ N. respectively). In this paper, observe N E {10, 20). For each experiment across all models, rum training and evaluation for five rounds with different random seeds and report the average performance.
Baselines. We include BPR 43 as the standard collaborative filtering method, and include several representative sequential models, namely GRU4Rec (25) Bert4Rec (88. SASRee [29). LRURee (59), Mamba4Ree R4 We also compare with competitve LLM-enhanced recommendation methods: RLM 42 integrates representation learning with LLMs and aligns the semantic space of LLMs with the representation space of collaborative
relational signals. SAID [7 utilizes LLMs to explicitly learn semantically aligned item ID embeddings based on texts for practical recommendations. GENRE 37 employs prompting techniques to enrich the training recommendation data at the token level to boost content-basse recommendation.
Implementation Details. All experiments for recommender systems in this paper are conducted on the Tesla V100 GPUs. In the experiment, the MLLM used for item-wise tag extraction is M3 [8] and the LLM used for tag logie inference is Owwen2.5-7B-Instruce
[56] The LLM semantic embedding models used for the LL.M-based 40 from OpenAI, For TagCF training, we use Adam optimizer with a
baselines setteemeedding----mee
learning rate of le-3 and weight decay of 1c-5. We follow RecBole [X] as the implementation hack and reproduce all baselines with hyper- parameters from either the original setting provided by authors or fine-tuning using validation. For 256 and head size of 2.
encoder and tag-based item encoder,
blations. assess the individual contributions of three key components in our integration framework. we conduct an ablation study comparing the complete TagCF system with three variants, each excluding one component: tag-based encoder, tag-based learning augmentation, tag-logic inference, denoted as w/o TE, w/o TA, and w/o TLI, respectively. As demonstrated in Figure the experimental results confirm that all three components significantly enhance both recommendation accuracy and diversity metrics. The performance degradation observed in each ablated variant underscores complementary value of each module within integrated framework.
TagGF
Industry
Book
Movie
0.0275
0.20
NDCG920
0.0225
0.0200
0.0175
0.14
0.0150
TagCF-K It
TagCF-ut ut
TagCF-it it
TogCF-ut
TagCF-t
TagCF-ut
Industry
Book
Movie
0.06
0.9
Coverage@20
0.05
0.04
0.03
0.6
TagCF-It
TagCF-ut
TagCF-It
TagCF-ut
TagCF4t
TagCF-ut
Figure 8: The ablation results of the three key methods of the tag-logic integration module.
26

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TagCF-it
14
TBgCF-UC
58
3:20
220
MARG20
DCGGR1
MAR5220
NOG0910
Coverageck10.
Coveragee20
Cove Coverage
Figure 9: The impact of the number of top-k during inference.
We also conduct experiments with different number of tags extracted for each item (k (20, 50, 100, 200, full)) and present the results in Figure | solutions, we find that the full tag set achieves the best results, where TagCF-ir tends to focus on diversity metries and TagCF-ut addresses the accuracy metrics.
Though it might be impractical industrial
C Observations and Analysis
C.1
Statistics of Tag Sets
Table d summarizes the statistics of the full tag set of T and C, as well as the reduced cover set T and C*. Based the statistics provided in Table 6, find that item lags generally have shorter lifespan compared user tags. While the full tag sets both users and items continuously expand without removal, the daily expansions reveal key distinctions. Item tags exhibit a significantly higher daily expansion, indicating more frequent updates. In contrast, user tags have a much smaller daily expansion and have nearly converged in the cover set. This smaller set size with less frequent expansion suggests that user tags are more stable and have longer lifespan, whereas the high update frequency of item tags points shorter lifespan. In practice, we also find that tags follow an extremely skewed frequency distribution (Figure TT.. indicating that not all tags are identically useful and expressive. While a few general tags may be retrieved by a large number of items, there also exists large number of precise but unique tags that cannot cover a sufficient number of items. As illustrated in section 3.1] this motivates design of the cover reduction module. In practice, the open full set size tends expand considerable even after the 30-day observation period, while the reduced cover set quickly converges in the first few days.
Table 6: Tag set statisties in our industrial platform
type
| full size
daily expansion
cover set size
cover set daily expansion
tag
2,976,845
300-300K
7,633
converged hundreds
item tag
50,208,782
3.5-4.0 million
20,956
Figure 10 case study comparing the original video content with corresponding generated tags. The figure demonstrates that both the user tags and item tags produced by the MLLMs are highly expressive and of superior quality, effectively capturing the video's 's key attributes.
Tag Case Study:
To validate the reasonableness of the tag set distribution extracted by the MLLMs, we analyze the frequency distribution of tags. as illustrated in Figure
Tag Frequency Distribution (Left Plot): The left plot shows the tag frequency distribution, where the r-axis represents individual tags ordered by their IDs, and the y-axis corresponds to the log-scaled frequency of occurrence. From the tag frequency plot, we observe the distribution follows a pattern consistent with real-world tag systems (c.g., a plwer-law distribution), as both User and Item Tag curves exhibit a steep decline in frequency as Tag ID increases. This indicates that: A small subset of tags dominates (e.g., common tags like "elegant" or "cheap" Long-tail tags (high Tag ID) are but exist, indicating diversity in generated tags.
Tags per Item Distribution (Right Plot): æ-axis showing the number of tags per item and y y-axis representing the log-scaled frequency of items associated with each tag count. The peak observed at 1.0-1.5 (log scale) suggests that most items are assigned 3-5 tags (since 10 ~ 3, 10..5 z 5). This balanced tagging behavior, neither overly sparse nor excessive, enhances the usability of the generated tags for downstream recommendation tasks.
The right plot displays the distribution of tags per item, with the
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Tag Biamppe
Nacure frewwry
viDeo kakkms- bexdfu coace ondrnour toresopo.
Beacnes and Coaeciios, The vilte shomcianes so meing bouth mestiee
epooding eide
reann boudy.
Bearty: eerested crylaa dierr
dest odfocc
trout reluraal
land
vouwery uzoniing to nelure tiera
Boach and Activity Crdhualeeri The sandy showe
waker augedt vituo
Vecetton
besch selling pageat
roide
Foy ind beach mordd achubici ournning ng
and water sports
pi and antud those oboniing or mvoingg gebecyyy
and Endhrollnte The
laclere the tira viea right
Offects:
stase
caver route lauay holds, edtccing these monndded suc' vocze meditioe.
axderoos Fermuadd vaualats Cot
dione dodal akroophoce provde suily to thii experioce appeating to owk intaatiie
Advillstioo sprans sha for viwerr sooiing ig sooff wiuul contort
nou vinue prrviii calising
sinwes wer a carigg chevnd e fari be
bnownnn apo
Unea ratanal
Nebuvel Seerery The eider shore yau tooonigg ng Boos and fuon pronery creut
more walty figh-dein loulage matk rea detal of
pandoesig sults, uttottig Rise possirnne uboul for fuuee
trees pratiiy
iii-ddeter on
retuudyy if foua shote
Cardbeiing Technique:: video darwrrtaaa and proned and
perkeciiy Rover uhokgraaht
atudy
perfed Landeruue Prpairenn who huturn Bontooard grew wnyle rature autiing
video dhou pepeoppppyyyyy mire
Gurmar Activit Lovera: The castarr aitsery and matural envorman to viowar who
culber
wfe unplenng
arplersioo..
and tam
garders wit
rea for thsiin desis esturases
ormance es foor ipeon, sppeeting to velwar interatte in landhee no powareet
Oealge: Theug ihu food arrngemannss and piant aiora sable
Blssuliicatoon
Blustrase fows
r. Poreets and Expncting Porvese The video colleccoe of ovely cranse sppeale thees
Klds fanhioe: Ciarty Incharas that the video fumanes chideenis ditting
Pilicc Driws: Highighss pince eiy drasss givs ancwodsed
2. fuuinity and disty Product Erehusllste Uper with strong semec parm and
Gahaarn's Day dit Stoggerr Thuss daery un kpping ug soic al piu fuit duightes
daity Seniiy Outiis heaann oa drue's ortle walutle for eall to wow-
Stnyaniteee aloess Those sows pay specia aroution the condicniiinnn and Mayn Qiveity diser Fuskion dooker Theve ryrigtts velicae ertats poonurr dkring appooing froo perpooo high cwrdeers
ietusaid wiker sid
savn who folne swweling oo
Figure 10: The example of tags generated by the MLLMs.
Dnstriutiin Prequency
sttibbuttoo
1.08
L.0
Lue
1.5
Figure 11: Item tag and user tag frequency distribution.
C.2 Statistics of Logic Graph
We also investigate the quality of the logic graph by analyzing the edge degree of the U2I and I2U logic graph in Figure [2 The left is a scatter plot with marginal distributions of the U2I graph. Since the U21 graph represents a direcied graph of user tags to item tags conversion relationships, the x-axis indicates the out-degree of use tags, and the y-axis indicates the in-degree of item tags. The right is scatter plot with marginal distributions of the I2U graph, with the x-axis indicates the out-degree of item tags and the y-axis indicates the in-degree of user tags. We observe that in the U2I graph, the out-degree distribution of user tags highly dispersed, indicating that user generated tags reflect personalized social roles rather than conforming to homogencous labeling patterns. This suggests that the divergent logic of the U21 graph can cover a broader range of item tags, mitigating the "clustering effect" and thereby breaking through information filter bubbles to enhance the diversity of recommendation results.
C.3
Case Study of Tag-Logic Exploration
Intuitively, the initial tags (:) represent the most obvious type of users that the item would match, while (((1) diverges from the initial user roles which tend to explore outside the echo chamber [19 in the recommendation process. We provide real case example in Figure 13 to illustrate the differences. We consider both sets as
effective tags of the corresponding item and we define positive/negative tags for the positive/negative items as:
Tet = 7: UT!!) T,- = T: UT!")
(13)
where the weights of the same tag are summed and normalized.
We further present case study in Figure 13 that illustrates the transformation from users' original tags exploratory tags during the inference process. Specifically, we compare: (1) the original user tags predicted by the model for the target user, and (2) the logically explored user tags from the initial user tags. The results demonstrate that the purple-hign highlihet tags in the exploration tag set (Wedding Preparers. Parent-child Activity Participants and Sking Enthusiast) successfully break through the information cocoon of the original tag
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Degiee
Degies Analysi 120 Craph
Figure 12: The degree analysis in U2I and I2U graph.
collection, introducing three novel semantic dimensions, Correspondingly. the expanded recommendation list incorporates fresh short vidcos aligned with these novel tags, ultimately delivering innovative user experience through logic discovery.
T(0)
T
Dynamic Dance Enthustast
Chanoogapher
Romance Story Enthuslast
Explore outside
Prepwrers
Outside the
Parent:child Activity Participants
Fashrio Enthustast
Outdoor Enthusiast
User
SportsTech Enthustast
Outside the
Cycling Enthusiast
Echo Chamber
Fastron Enthusrasts
Recommendation List
Recommendation List
Dynamic Dance Video
Dynamic Dance Video
Romance Novel Sharlng Video
Explore outnille
Children's Danca Growth Record
Stytish Cutfits Video
Stylish Dress Video
Outfit Inspo Video
Weddling Video
sharing
Cycling Scmnery Video
Cycllng Scenery Video
Skiligg Video
Figure 13: Case study on a user's original (user)tags and exploration (user)tags during inference.
D
Additional Evaluation Results
D.1 (M)LLM Evaluation with Human Experts
Table Generated tag comparison results against GPT-4o.
Test Set
(G+SM(B+S) (G+SV(B+S)) 95% CI Win-Tic Rate G/S/B Details
359 videos
0.92
[0.993.1.35]
59,88%
125/90/144
Although multimodal large models have demonstrated strong capabilities in content understanding and reasoning for short vidcos, they may still suffer from hallucination issues this stage. To validate the quality of the tags extracted by the MLLMs, we conducted a manual GSB Evaluation (Good Same Bad) (26) and a a fine fine- grained evaluation to assess the quality of the tags generated by the MLLMs. This manual evaluation was performed by trained professionals who systematically scored each output tag against predefined criteria. Specifically. we selected test of 359 short videos and compared the fine-grained scores of tags generated by our method with those generated by GPT-4o 28 The human evaluation consists of two parts: GSB assessment on the full set of 359 test samples (shown in Table and a fine- ine-ggained evaluation on a subset of 191 samples (shown in Table B). The fine- fine-grained criteria include four dimensions: Accuracy, Completeness. Reasonableness. and Interpretability.
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Evaluates whether the model's output contains errors for example, extracting tags from the video title image OCR that completely unrelated to the video content (ignoring weak relevance; only considering obviously incorrect labels).
Accuracy:
Completeness: Assesses whether the model's tags cover all key aspects of the video content -.e., whether any important dimension is missing.
Reasonableness: Refers to cases where tags are not outright wrong but are only weakly related to the video's main theme (c.g,, mentioning incidental or background elements).
Interpretability: Mcasures whether the tags are easy to understand, using clear and concise language while avoiding vague or obscure expressions.
Table 8: Fine-grained tag quality comparison
Test Set
Model
Accuracy
Completeness
Reasonableness
Interpretability
191 videos
V1
0.88
0.65
0.93
0.99
191 videos
GPT-4o
0.85
0.75
0.92
0.99
The human evaluation results demonstrate that in terms of overall effectiveness, method achieves GSB fine- ane-grained level, our approach outperforms GPT-4o accuracy,
score 0.92 compared to GPT-4o. shows slightly lower performance in completeness, and performs marginally better in reasomableness. These results substantiate the superior quality of the tags extracted by our method.
Note: Compared with tag extraction, we keep higher tolerance for the factual accuracy of the generated logic graphs, as their primary objective is facilitate user interest exploration. This goal prioritizes diversity and the stimulation of potential user interests over striet factual precision. Nevertheless, objectively assess the quality these graphs, we conducted a corresponding human evaluation study. The results on a test set of 3,220 videos are summarized in the table 9
Table 9: Tag logic graph comparison results against GPT-4o. (G+S)(B+S) (G+SV/(++) 95% CT Win-Tie Rate
Test Set
G/S/B Details
3,220 videos
0.875
[0.955, 1.19]
52.3%
1237/500/1483
D.2 Different LLM Size & Complexity
Different LLM Size: To determine the optimal L.M size for TagCF framework in an industrial setting, we conducted extensive experiments with LLMs various parameter scales, including 0.5B, IB, 7B, and versions. The key findings from our scaling study are summarized in the table [0] Our parameter scaling experiments found that while smaller models (0.5B/IB) handle 93% of cases, the 7B model is crucial for the hardest 7%. The 9B model offered only marginal +3% accuracy gain but with significantly higher latency. Thus, we employ cost-cffective cascade smaller models for easy cases and the 7B model for hard samples, achieving an optimal balance.
To optimize computational efficicncy, our system performs tag extraction a threshold- based manner, processing only new videos that exceed a predefined interaction count (e.g.. 500 interactions). This selective approach ensures that resources are allocated to higher-impact content while maintaining tagging quality. Furthermore, model distillation is employed enhance inference efficiency, enabling the distilled model extend coverage to all videos cfst-effectivel.. For online integration, leverage highly efficient key-value (KV) database, which allows for the retrieval of video's associated tag set in constant O(1) time complexity. Our online workflow is designed to facilitate parallel reading of tags and subsequent modeling computations. Crucially, once extracted, the tags stored as immutable metadata permanently linked to the video. This all downstream recommendation tasks throughout the video's lifecyele, significantly enhancing the overall performance reusability within the system.
Complexity:
persistent tag
Table 10: Different LLM Size Results (7B as Baseline)
Model
Accuracy
Coverage
Hard Case
Relative Cost
0.5B / 1B
-7%
-7%
x
-69%
7B
V
9B
+3%
+0%
V
+37%
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E
Broader Impacts
Our work enhancing recommender systems through LLM-enhanced user role identification and logical Recommendation has significant societal implications, both positive and negative. By incorporating roles and behavioral logic, our framework enables more nuanced recommendations, better aligning with individual preferences and social contexts. This can enhance user engagement and satisfaction applications such as e-commerce, content platforms, and educational tools. On the other hand, the framework may potentially provide new methodologies to social science by providing automatic and systematic solutions to discover user behavioral logic in the big data
However, despite the advancements offered by our method, it essential acknowledge potential drawbacks. the system misinterprets user roles behavioral logic, could lead to irrelevant harmful recommendations. Additionally, concerns regarding privacy and fairness arise due to the collection and analysis of user data for recommendations, necessitating careful consideration of ethical implications in its deployment. To this end, further complementary research the solutions mitigate these issues is necessary to achieve a benign and protective recommender system for
F
Limitations and Future Work
Deal with cold start users: In this work, focus standard top-N recommendation task that assumes the presence of user histories. The proposed TagCF also involves tag based user encoder that uses a sequential model backbone. Thus, in cold-start user scenarios, where the users provide little information about their preferences, it would be difficult to solve the user role identification task or to investigate which logic the user follows.
Improving expressiveness of the tag set: TagCF can obtain sufficiently expressive and general tag-logic knowledge that transfer other tasks augmentation models. Yet, we are skeptical about the optimality of the extracted knowledge, mainly due to greedy cover set update algorithm.
Computational cost: All three modules in our proposed framework brings extra computational overheads to system. The tag extraction module and the logic reasoning module involves the inference cost of MLLMs and LLMss. However, due to the generalizability of this tag-logic knowledge, they can benefit many other task across the platform. This is also onc of the key reason the augmentation paradigm of LLM-based recommender system are most favored in recent days. On the other hand, the tag-logic integration module requires extra efforts model the tag-based encoder, learn additional objective, explore tag-logic during inference. These are all inevitable computational costs that the designer have to consider when constructing cost-effective solutions.
Full tag set vs. cover set: For efficiency and generalizability concerns, TagCF adopt the cover sets for tag-logic representation and augmentation of recommender systems. However, the cover set only takes small portion of the full set, which leaves the majority of the full set knowledge unused. Intuitively, is reasonable to believe that the more fine-gre ine-grained full tag set may potentially have better interpretability specific cases, and it may work investigation on better ways to exploit this full set.
31


Reviews:
[{"review_id": "zNW33h32ys", "reviewer": "Reviewer_vTUY", "summary": "This paper introduces the user role identification task and behavioral logic modeling task through an integration framework of (multi-modal) large language models. In addition, taking advantage of the extracted tag-logic information can further augment the recommendation performance. Experiments conducted both online and offline demonstrate the effectiveness of the proposed framework and empirically show that user role identification is potentially a good choice.", "questions": "1. Why the logic reasoning only depend on the open-world knowledge of LLM, ignoring the important collaborative filtering in training data?\n2. In Eq (4), why use ID-based item embedding X_i as the representation of the item instead of tag-based item encoding or the combination of both ID-based and tag-based item embedding?\n3. Why tag-based learning augmentation can benefit recommendation performance? Is there any more in-depth discussion or analysis?", "limitations": "The authors have discussed most of limitations of their work, but lacking discussion on user privacy concerns.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The evaluation of the real-world online scenario demonstrates significant credibility to the practical value of the proposed framework.\n2. Various experiments are conducted, including detailed hyperparameter analysis, ablation studies, and comparisons across multiple baselines.\n3. The paper is well-organized and easy to follow.\n\nWeaknesses:\n1. The motivation for incorporating the user role identification task needs more in-depth discussion or theoretical support. For example, whether the traditional ID-based or profile-based user embeddings already implicitly contain user role information? Why is it necessary to explicitly identify user roles? \n2. Some experimental phenomena lack deeper analysis. For instance, why do TagCF-util and TagCF-expl perform significantly differently in terms of interaction and diversity? It would be better to provide some more discussions. In addition, in lines 312-313, this paper claims that \"item tags may have a shorter lifespan and may frequently update even in the cover set\". Detailed support should be provided for this claim. \n3. The introduction of user role identification may raise user privacy concerns, which is particularly critical in privacy-sensitive scenarios.", "was_revised": true, "final_justification": "I have read the authors' rebuttal and keep my original rating.", "rebuttal": "We thank the reviewer for the positive feedback and constructive suggestions and we address the comments as follows:\n1. ***Motivation of user role identification***:   \n- We used to have the same concern as the reviewer's, and that is also the reason we conducted various rigorous analysis including the statistical comparison in section 3.2.5 and Appendix D.1, as well as the comparison in the main result of Table 1. Based on all these empirical results, we believe that we have provided an evidence that user role identification and tag-logic extraction might indeed be an missing part of the current recommender system design.  \n- Intuitively, although ID-based and profile-based user embeddings may implicitly contain user role information, they still exhibit certain limitations including but limited to inaccurate modeling of user behaviors (as the examples shown in introduction), insufficient interpretability from black-box solutions, and lack of explicit strategic control over the recommendation policy (compared to the tag-logic inference extension). As we have mentioned in the paper, some interpretability-oriented solutions[1,2] may partially address these limitations, but most existing approaches rely on confined feature engineering within closed domains, resulting in limited real-world applicability and lacking of generalizability.    \n- Empirically, in Table 2, we compared against ID-based (Mamba4Rec, the state-of-the-art in this category) and profile-based user embedding methods (RLMRec which leverages LLMs for user representation). While these baselines demonstrate competent performance, they remain suboptimal compared to our method. In general, we believe that comprehensive role identification and behavior logic discovery may complement the current ID-based recommender system.   \n- Additionally, in industrial perspective, the user role knowledge and user behavior logic graphs are originated from recommendation data but potentially has broader impact for all video producers, various businesses or even social study.\nIn general, we sincerely appreciate the insightful comments and we will integrate this motivation discussion more explicitly into the revised manuscript, particularly in Introduction and Method Section to strengthen the justification for our role-driven approach.   \n[1] Explainable recommendation with fusion of aspect information, WWW, 2018   \n[2] Triple Dual Learning for Opinion-Based Explainable Recommendation, TOIS, 2023\n\n2. ***TagCF-util vs TagCF-expl***:   \nIntuitively, the initial tag set comes from the user's past behavior so it best aligns with the user's ``echo chamber'' (or the observed or presented interest by user), while the TagCf-expl uses the logic graph to extend these tags to a new set of tags which is likely to be different from the initial set, achieving better diversity during recommendation. Due to the limited space, we have provided a case study to better illustrate this differences in Appendix D.3 instead of the main body of the paper.\n\n3. ***Support for shorter item tag lifespan***:    \nThe full set continuously expand without removal, and the differences in the daily expansion number in Table 6 can partially supports this claim. For coversets, item tag's daily removal is around hundreds, but the user tag have almost converged and may never update for days. Intuitively, a smaller set with less frequent removal usually indicates a more stable tag set with longer lifespan. We will add this statistics in Table 6 as additional support and provide additional discussion of this point in the appendix. \n\n4. ***CF data vs. open-world reasoning***:   \nWe intentionally make the tag-logic extraction process independent of collaborative data to better discover real-world knowledge that enhances recommendation but are not typical in the collaborative filtering data. This is also why we consider the graphs as general knowledge that are transferable to other datasets. Still, we do not rule out the possibility of using collaborative filtering data for logical reasoning, so your question also raises a key future direction that compares different knowledge extraction methodologies for the TagCF framework: end-to-end logic generation with CF vs. LLM-based logic + CF alignment. Thanks for your attention and suggestions.\n\n5. ***ID Embedding in Eq.4***:   \nIn lines 649-651, we have mentioned that any feature encoding fusion method is theoretically feasible but empirically separate design achieves the best result. Yet, we do not observe significant difference for different embedding schemes for the target item embedding in Eq.4, so we keep the original design that uses the ID embedding since we are calculating the raw score. We previously experimented with tag-based item encoding and the combination of both ID-based and tag-based item embedding, but the results ultimately showed that this fusion method is the best. \n\n6. ***Why tag-based learning***:   \nTag-based Learning augmentation helps in the alignment of embedding spaces by ensuring that the user and item embeddings are consistent with the tag embeddings. While we have provided empirical proofs for this design, intuitively, we believe that this might be related to the generalizability for tag-based contrastive learning compared with noisy item-based learning. Nevertheless, the fundamental reasons for this effectiveness may require further investigation in the future.\n\n7. ***Privacy issues***:   \nIn our current system design, we consider the recommender trustworthy and benign to users, but we agree with the reviewer that this might not always be the case when users do not trust or partially trust the system. We believe that the privacy issue could be one of the key future direction for TagCF, and standard solutions like encryption methods or adversarial methods may all need specialized designs. We will include this discussion in the limitations section and we thank the reviewer for bringing this perspective which significantly improves our work's completeness."}, {"review_id": "O6so5Zkkog", "reviewer": "Reviewer_xZHV", "summary": "The paper introduces a novel recommendation framework called TagCF, which aims to enhance the performance of recommender systems by explicitly modeling user roles and the logical relationships between user roles and item topics. Traditional recommender systems often overlook the importance of user characteristics and their social roles, focusing primarily on item topics and user-item interactions. This paper addresses this gap by proposing a solution that integrates Large Language Models to extract user and item tags and infer logical connections between them.", "questions": "Please refer to Weakness seciton.", "limitations": "This study may raise some fairness issues, such as LLM-inferred user roles (e.g., \"low-income parents\") could propagate demographic biases.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper proposes an innovative framework, TagCF, which effectively addresses the limitations of traditional recommendation systems by incorporating multi-modal large language models (MLLMs) and recommendation systems, thus capturing user roles and social roles.\n2. The experimental approach is sound, with access to both industrial data and easily accessible public datasets. The effectiveness of the framework has been validated in the industrial setting, and the ablation studies and hyperparameter analyses are quite comprehensive.\n3. The paper elaborates on each component of the TagCF framework and its working mechanism, including MLLM-based tag extraction, LLM-based collaborative logic filtering, and the integration of tag logic into recommendation systems.\n\nWeaknesses:\n1. The paper utilizes the reasoning ability of LLMs in constructing logic graphs, it does not provide detailed solutions for ensuring the accuracy and consistency of the logic graphs, or for handling logical conflicts and contradictions.\n2. The paper mentioned that Qwen2.5-7B was used to iteratively infer the U2I and I2U logic. Have you tried to use models with other parameter values, such as 3B or 9B, to explore the impact of different large model parameter values ​​on reasoning performance and the robustness of the entire framework?\n3. The experiments on public datasets only selected Books and Movies, while the industrial application is based on video recommendations. It is suggested to supplement the experiments with two public video recommendation datasets, Microlens and PixelRec, to further demonstrate the effectiveness of the framework in video recommendations.\n4. It is suggested to present a case study to illustrate the “Dynamic Cover Set Reduction Algorithm” mentioned in the paper.\n5. Daily MLLM processing of 3M videos requires 50× NVIDIA 4090 GPUs (§C.1). While distillation mitigates this, cold-start tag extraction for new items remains costly.\n6. Logic-based tag expansion involves graph traversals – latency impact on ranking stage is unquantified.\n7. Are there fairness risks? LLM-inferred user roles (e.g., \"low-income parents\") could propagate demographic biases.", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the positive feedback and constructive suggestions and we address the comments as follows:\n\n1. ***Accuracy and consistency of graphs***:   \nAs we have illustrated in Appendix E, we validated the correctness of the generated tags of videos with both human evaluation and larger model (i.e. GPT-4o) comparison. **In contrast, we give more tolerance to the correctness of graphs, since the main purpose of these logic graphs are interest exploration, which is more diversity oriented rather than accuracy oriented.** To be more rigorous, we also conduct expert human assessments on the logic graphs generated by our LLM, with comparative analysis against GPT-4o's reasoning outputs. We conducted a manual GSB evaluation (Good Same Bad)  to compare the accuracy of logic graphs generated by our large model versus GPT-4o, specifically assessing whether inferred tag relationships were erroneous (e.g., cases where inferred tags were semantically or factually irrelevant to original tags). As a clarification, we will add the following quantitative results to our paper to enhance the completeness of our work.\n| Test Set    | (G+S)/(B+S) Ratio | 95% CI     | Win-Tie Rate | G/S/B Distribution (Counts) |\n|:------------:|:------------------:|:-----------:|:-------------:|:---------------------------:|\n| 3,220 videos| 0.875             | [0.95, 1.19] | 52.3%      | G:1,237 / S:500 / B:1,483   |\n\n2. ***Different LLM size***:   \nYes, we extensively tested various parameter sizes  (including 0.5B, 1B and 9B versions). We found that smaller models (0.5B/1B) can achieve reasonable accuracy for 93% of cases, while the 7B variant remains essential for resolving the most challenging 7% of samples where smaller models fail. Although the larger 9B model provided a slight improvement in accuracy (+3%), it also increased inference latency, making 0.5~7B the optimal trade-off. To reduce costs, the industrial setting uses smaller 0.5B/1B models for the first generation, then uses the 7B to resolve the hard samples. The resulting mechanism reduces operational costs by 37% compared to the 9B model, offers 98% video coverage and maintains production-grade latency. This balance of cost and performance led us to standardize on the 7B version. We will provide detailed parameter scaling results in the appendix. \n\n3. ***Other video recommendation datasets***:   \nDue to the time limit, we can only provide a preliminary results for MicroLens-50K, a small version of one of the datasets mentioned by the reviewer. We present the results of the best baselines and our method in the following table. As shown below, our method has also demonstrated its effectiveness on this public video recommendation dataset. Note that the I2U and U2I graphs are transferrable, but one still have to run large models to generate tags to use these features.\n| model | NDCG@10 | NDCG@20 | MRR@10 | MRR@20 | Cover@10 | Cover@20 | GINI@10 | GINI@20 |\n|---|---|---|---|---|---|---|---|---|\n| Mamba4Rec |0.0328 |0.0403 |0.0215 |0.0239 |0.8037 |0.8510 |0.7796 |0.7734 |\n| SAID |0.0332 |0.0410 |0.0220 |0.0241 |0.7952 |0.8378 |0.7902 |0.7880 |\n| TagCF-it | 0.0357|0.0439|0.0236 |0.0252 |**0.8511** |**0.8804** |**0.7198** |**0.7302** |\n| TagCF-ut | **0.0368**|**0.0452** |**0.0243** |**0.0264**|0.8414 |0.8751 |0.7264 |0.7350 |\n4. ***Dynamic coverset algorithm case study***:   \nThe dynamic nature is mainly driven by the continuous addition of new tags to the cover set and the removal of outdated tags. To provide a clearer understanding, we have included a case analysis in the table below and we plan to incorporate both the table and a detailed case analysis in the revision phase. The table presents the statistics and adjustments of the cover set after applying the Dynamic Cover Set Reduction Algorithm.   \n| Cover Set Statistics | Results | Description |\n|---|---|---|\n|Previous Video Coverage Rate | 99.00% | This indicates the coverage rate of the cover set before applying the algorithm|\n| Previous Video Recall Rate | 80.34%  | This indicates the recall rate of the cover set before applying the algorithm |\n| Updated Video Coverage Rate | 99.39% | After optimization by the algorithm, the coverage rate of the cover set slightly improved|         \n| Updated Video Recall Rate | 98.61% | After optimization by the algorithm, the recall rate of the cover set significantly improved |\n| **Cover Set Dynamics** | **Results** | **Description** |\n| Outdated Tag Count | 182 | These are the tags removed from the cover set because they did not appear in the historical records |\n| Remaining Tag Count | 22,757 | This is the number of tags retained in the cover set after optimization by the algorithm.\n\n5. ***Cold start tag extraction cost***:   \nFor new videos, their multi-modal information will first generate the embedding through M3 (described in section 2.1). Then the distilled model will use the this embedding to infer corresponding tags in the cover set, which is very efficient in practice and it is able to cover all videos. If you are referring to the tag extraction with pure MLLM in the full open language space using Eq.1, then there do exists a computational bottleneck. To circumvent this in practice, we perform pure MLLM-based tag extraction only on videos exceeding 500 interactions, rather than applying indiscriminate extraction across all uploads. This threshold-based approach ensures computational resources are allocated to higher-impact content while maintaining tagging quality. Fortunately, we only need to run this process once for each video, and the tag features will serve all downstream tasks throughout the video's lifecycle. We will add this implementation detail in the revised manuscript.\n\n6. ***Quantify graph traversal***:   \nIn our implementation, both video-to-tag and tag-logic graphs are stored as large key-value (KV) lookup tables, and the parallel lookup process ensures that the time complexity for this lookup phase is $O(1)$ (including the initial tag set and the exploration tag set). When calculating the tag-based scores, the overall traversal complexity is $O(b^2 k)$ (see Lines 665-668), which is the same as the size of $\\mathcal{T}(1)$, and one can boost this process using parallel computing (mentioned in Appendix B.6). During the model inference stage, tag scoring is performed in parallel with other scoring processes, which usually requires computation of more complicated models. Therefore, there is no additional latency increase in our online experiments.\n\n7. ***Fairness issues***:   \nYes, we acknowledge the existence of the potential demographic bias propagation through LLM-inferred user roles and item tags. This is partially reflected by the power law distribution of tags in Figure 11 of Appendix D.1. In response, we will expand our limitations section with the discussion on potential fairness issues. We thank the reviewer for bringing this perspective which significantly improves our work's completeness."}, {"review_id": "ndwLkJrnzU", "reviewer": "Reviewer_GKEQ", "summary": "This paper introduces TagCF, a novel framework for recommender systems that aims to move beyond traditional item-correlation models. The authors argue that mainstream approaches neglect the crucial context of a user’s real-world social roles (e.g., “new parent,” “musician,” “student”), which are often the logical “confounders” explaining their interests. TagCF addresses this by explicitly modeling user roles and the logical relationships between roles and item topics.\n\nThe framework leverages Large Language Models (LLMs) in a multi-stage process:\n\n- Tag Identification: A Multi-modal LLM (MLLM) analyzes items (e.g., videos) to automatically extract two types of descriptive tags: item-topic tags (e.g., “Folk Music Shows”) and user-role tags (e.g., “Folk Music Lover”).\n- Logic Modeling: A separate LLM infers the logical connections between these user and item tags, creating a “collaborative logic graph.” This graph models relationships like “a Folk Music Lover is likely interested in Instrument Shows” (U2I logic) and “an item about Bow Instruments is relevant to a Symphonist” (I2U logic). To handle the massive scale, the system uses techniques like cover set reduction and model distillation.\n\nThis extracted tag-logic knowledge is then integrated into a standard sequential recommender system to enhance performance. The integration happens through tag-based encoders, a contrastive learning objective, and a logic-based scoring extension during inference. The authors demonstrate the effectiveness of this approach through extensive offline experiments on public and industrial datasets, and critically, through a large-scale online A/B test, showing significant improvements in user interaction and recommendation diversity.", "questions": "- Tag Pool Generation and Maintenance: The paper describes a dynamic cover set reduction algorithm (Alg. 1) to maintain a stable and expressive set of tags (T*, C*). However, the process still seems complex. Could you clarify how the initial, unrestricted tag sets (T, C) are managed? Do they grow indefinitely, or are there pruning mechanisms beyond the D-day history check in the cover set? How sensitive is the final recommendation performance to the cover set threshold τ and the history window D?\n- User Role vs. Item Topic Stability: A key claim is that “user role tags are empirically more stable and more expressive in representing personalities” than item tags. This is supported by the smaller cover set size (Table 6) and superior accuracy of TagCF-ut (Table 2). Could you elaborate on why you believe this is the case? Is it because user roles are more fundamental and timeless concepts (e.g., “parent,” “student”) compared to item topics which can be ephemeral trends (e.g., a specific meme or news event)? A deeper discussion on this point would strengthen this important finding.\n- Baseline Comparison and Feature Ablation: The online baseline is described as a “state-of-the-art ranking system.” It is crucial to know if this baseline already incorporates some form of user role or persona modeling, even if manually defined (e.g., using user profile data or clustered behavior). If so, your method’s improvement is even more impressive. If not, could the performance gain be partially attributed simply to adding any form of user role information, rather than your specific LLM-based approach? An ablation that compares TagCF-ut to a simpler baseline using predefined user roles would be very insightful.", "limitations": "Yes, the authors have adequately addressed the limitations and potential negative societal impacts of their work in Sections F and G. They have been upfront about several key challenges:\n\n- Cold-start users: They correctly identify that the user-history-dependent model struggles with new users.\n- Optimality of the tag set: They acknowledge the greedy nature of the cover set algorithm might not be optimal.\n- Computational cost: They are transparent about the significant overhead introduced by the framework.\n- Broader Impacts: They responsibly discuss the dual-edged nature of their work—improved personalization vs. potential for misinterpretation, harmful recommendations, and privacy concerns.\n\nOne critical point that could be added or emphasized is the dependency on the specific MLLM/LLM chosen. The quality and nature of the extracted tags and logic graphs are entirely dependent on the underlying “world knowledge” and potential biases of the base models (M3, Qwen2.5). A different LLM might produce a completely different logic graph, leading to different recommendation outcomes. This reliance on proprietary or rapidly evolving models is a key limitation of this entire line of research.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- Quality & Significance: The paper tackles a significant and well-motivated problem: moving beyond simple correlational models to incorporate deeper, more interpretable “logical” reasoning into recommendations. The proposed tasks of “user role identification” and “behavioral logic modeling” are a valuable conceptual contribution. The work is particularly strong due to its extensive and convincing empirical validation, including a live A/B test on a massive industrial platform, which is a rare and high bar for academic papers.\n- Originality: The core idea of using LLMs to explicitly model a bipartite graph of user roles and item topics, and then reasoning over this graph to improve recommendations, is highly original. While other works use LLMs for recommendations, TagCF’s focus on identifying latent user roles as the logical bridge between items is a novel and powerful paradigm. The practical solutions for industrial-scale deployment, such as cover set reduction and model distillation, add to the paper’s originality and practical value.\n- Clarity: The paper is well-written and structured. The motivation is clearly articulated with an intuitive example (Figure 1), and the proposed TagCF framework is broken down into logical, well-explained components (Figure 2). The authors do a good job of explaining both the high-level concepts and the low-level implementation details.\n\nWeaknesses:\n\n- Clarity on the role of LLMs: The paper states it uses a pre-trained MLLM (M3) and an LLM (Qwen2.5) “without finetuning” to preserve their world knowledge. However, it then immediately introduces distilled models (Pθ for tag extraction and Pφ for logic reasoning) that are trained on the LLM outputs. This could be clarified. The key weakness is the complexity and potential for information loss in this multi-stage distillation process. The performance is highly dependent on how well these smaller, specialized models can capture the knowledge of the giant, generalist LLMs.\n- Complexity: The overall system is quite complex, involving multiple LLM/MLLM calls, two types of tag sets (full vs. cover), daily updates, model distillation, and three separate integration modules into a standard recommender. While the results justify the complexity, it represents a significant engineering and computational overhead, which might be a barrier to adoption for others.\n- Analysis of Failure Cases: The paper presents strong positive results but would benefit from a deeper analysis of the system’s failure modes. When does the LLM-based tag extraction fail or produce “hallucinated” tags? When does the logic graph lead to poor or nonsensical recommendations? Discussing these limitations would make the work more complete.", "was_revised": true, "final_justification": "I will keep the score to be the same and really appreciate the efforts the authors put into the rebuttal. I just read them all.", "rebuttal": "We thank the reviewer for the positive feedback and constructive suggestions and we address the comments as follows:\n\n1. ***Clarity on the role of LLMs***:   \n- In our method, only the distilled models $P_\\theta$ and $P_\\phi$ are trained, while the (M)LLMs (M3/Qwen2.5) remain frozen (see Lines 118-125, 605-623). As a result, we do not include parameter $\\theta$ and $\\phi$ in Eq.1 and Eq.2. \n- We'll clarify this architecture separation more prominently to address this confusion.\n\n2. ***Complexity***:   \n- To optimize processing efficiency, we selectively perform tag extraction only on new videos that exceed 500 interactions. This threshold-based approach ensures that computational resources are allocated to higher-impact content while maintaining tagging quality. Additionally, model distillation further enhances the inference efficiency of our method and it is able to cover all videos. For the integration modules, we employ a highly efficient KV (Key-Value) query database in our online system, enabling us to retrieve the tag sets corresponding to videos with $O(1)$ time complexity. Our online workflow also facilitates parallel tag reading and modeling. Importantly, the extracted tags become immutable metadata permanently associated with the videos, serving all downstream tasks throughout their lifecycle and significantly enhancing overall system performance.   \n- Though the distillation process introduces new stage into the system, we remind readers that there are two major purposes: 1) It reduces the tag information to a smaller, more stable, and more general coverset that better suits for downstream tasks including but limited to recommendation; 2) In systems with a large number of new items uploaded each day, it reduces the workload of MLLMs/LLMs. Conversely, this distillation process becomes unnecessary if the target tag set is small and given to the LLM and there is not many new items to process each day.   \n- We will provide a more detailed discussion of potential computational costs in the revised version of the paper. \n\n3. ***Accuracy of distilled model***:   \n- To mitigate the information loss of distillation, we ensure that the distilled models maintain a high level of accuracy by selecting the top-k predictions for video-to-tag mapping and top-b predictions for tag-to-tag mapping, typically k=10 and b=10 in our implementation. Empirically, the models can effectively fulfill the performance requirements. Additionally, by increasing or decreasing k or b, the distilled models introduce a trade-off between accuracy and inference speed.    \n- To provide further insight into this balance, we will include detailed accuracy/speed trade-off curves in the Appendix.\n\n4. ***Failure Cases***:   \n- We acknowledge the existence of possible hallucination cases. In Table 7, we provide a manual GSB evaluation, and in Table 8, we offer a fine-grained tag quality comparison between the tags generated by our system and those generated by GPT-4o. Though the accuracy is sufficient for production use, it is still not 100%. Fortunately, when the LLM produces \"hallucinated\" tags, it may not necessary affects the system's performance in a bad way, since the tag is still related to the video while being surprisingly different, which potentially improves the diversity. Still, one should try to minimize this error (e.g. by removing tags that contain incorrect information) to ensure recommendation accuracy.   \n- In contrast, the purpose of the logic graph is to explore user interests, so a certain degree of accuracy loss is permissible. We also investigate the quality of the logic graph by analyzing the edge degree of the U2I and I2U logic graph in Figure 12. To be more rigorous, we conducted a manual GSB evaluation to compare the accuracy of logic graphs generated by our large model versus GPT-4o, specifically assessing whether inferred tag relationships were erroneous (e.g., cases where inferred tags were semantically or factually irrelevant to original tags). The evaluation was performed on a sample set of 3,220 videos, and we will add the following quantitative results to our paper to enhance the completeness of our work.\n| Test Set       | (G+S)/(B+S) Ratio | 95% CI    | Win-Tie Rate | G/S/B Distribution (Counts) |\n|:-----------:|:------------:|:---------------:|:--------------:|:-----------------------:|\n| 3,220 videos   | 0.875             | [0.95, 1.19] | 52.3%        | G:1,237 / S:500 / B:1,483   |\n\n5. ***Tag pool generation and maintenance***:   \n- So far, we do not restrict the full tag sets (T, C) and only apply the D-day check on cover sets. In the full sets, we have observed a decreasing number of updates, meaning that the storage may eventually converge some day in the future, though in a much slower rate than the cover sets. Still, the size of these full sets is statistically much smaller than the number of videos. \n- Additionally, in practice, we find that tags follow an extremely skewed frequency distribution (Figure 11), indicating that not all tags are identically useful and expressive. This motivates our proposal of the dynamic cover set reduction algorithm (Algorithm 1) to maintain a stable and expressive set of tags (T*, C*). In terms of the removal mechanism, we only considered the D-day recall check but does not rule our the possibility of including other mechanisms in the future.   \n- We will provide more explanations for this algorithm in our revised paper to improve methodological clarity.\n\n6. ***Sensitivity of $\\tau$ and $D$***:   \n- These parameters are primarily determined by industrial requirements for all businesses rather than being tuned based on recommendation outcomes. \n- The threshold τ is designed to cover 99% of the videos, which is a critical industrial requirement. Achieving such a high coverage rate is essential for meaningful online application, and covering smaller number of videos would lead to significant information loss, making the system ineffective for business use.\n- The history window D is dictated by storage and resource consumption constraints. While different settings can be applied, a smaller history window increases dynamism, leading to instability in the tag sets. Setting a longer D helps improve tag stability and enhances recommendation performance.\n- We will address these concerns in our revised paper.\n\n7. ***User role vs. item topic stability***:   \n- Our claim about the superior stability and expressiveness of user role tags is supported by empirical observations: In Table 6, we present the statistics of the full user/item tag set and the reduced user/item tag cover set. It can be seen that the daily addition of full-size item tags is 3.8 million, while the daily addition of user tags is 0.26 million, indicating that the collection of user tags is more stable than that of item tags. For coversets, item tag's daily removal is around hundreds, but the user tag have almost converged and may never update for days. Intuitively, a smaller set with less frequent removal usually indicates a more stable tag set with longer lifespan. We will add this statistics in Table 6 as additional support and provide additional discussion of this point in the appendix. \n- We consider \"user roles are more fundamental and timeless concepts compared to item topics which can be ephemeral trends\" as the possible reason for this belief, but whether this is the true and only reason may require further investigation. So far, we choose to use the aforementioned empirical statistical results as verification.\n\n8. ***Baseline comparison and feature ablation***:   \n- Yes, our online baseline has already incorporated user profile data (such as user’s age, gender, etc.) with PPNET[1] and several manually designed tag features for videos similar to the related works mentioned in the paper. Note that the video-to-tag and tag-logic graph generation are merely based on the ''world knowledge'' of MLLMs/LLMs, which does not require the participation of the recommender system. This could be a potential advantage since it may provide signals and patterns that are useful but unseen in the existing solution, boosting the performance.   \n- We will include this detail in the revised version of the paper.   \n[1] PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information. (KDD '23)."}]

Author rebuttal:

Meta-review: This paper presents TagCF, a unified framework that augments recommender systems by explicitly modeling users’ real-world social roles and the logical relationships between these roles and item topics. Leveraging (multi-modal) large language models, TagCF extracts user and item tags and infers causal “confounders” such as “new parent” or “musician,” which traditional item-correlation methods overlook. Extensive online and offline experiments demonstrate consistent performance gains, confirming that user role identification is both effective and practically valuable for next-generation recommendation.

**Strengths**
1. To capture user role and social role, this paper proposes the TagCF framework. It involves MLLM-based tag extraction, LLM-based collaborative logic filtering, and the integration of tag logic into recommendation systems.
2. The evaluation is comprehensive. It includes both offline and online experiments. Component analysis is also covered. 
3. This paper is well-written and easy to follow.

**Weaknesses**
1. The motivation and some methodologies of this paper should be better clarified. Besides, the computational cost should also be discussed.
2. The experiments could be further improved by introducing more public datasets and case studies.
3. Reviewers have concerns about the LLM scalability and privacy.

During the rebuttal stage, the authors add some experiments to clarify the data and model size issues.

After the rebuttal, all the reviewers still have positive rating scores for this paper.
Decision: Accept (poster)