Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
Authors: Ting-Ji Huang
Jia-Qi Yang
Chunxu Shen
Kai-Qi Liu
De-Chuan Zhan
Han-Jia Ye
Venue: ICML 2025 poster (2025)
Keywords: multi-task recommender;out-of-vocabulary token;user representation
Primary area: deep_learning->foundation_models

Abstract: Characterizing users and items through vector representations is crucial for various tasks in recommender systems. Recent approaches attempt to apply Large Language Models (LLMs) in recommendation through a question\&answer format, where real items (eg, Item No.2024) are represented with compound words formed from in-vocabulary tokens (eg, ``item``, ``20``, ``24``). However, these tokens are not suitable for representing items, as their meanings are shaped by pre-training on natural language tasks, limiting the model's ability to capture user-item relationships effectively. In this paper, we explore how to effectively characterize users and items in LLM-based recommender systems from the token construction view. We demonstrate the necessity of using out-of-vocabulary (OOV) tokens for the characterization of items and users, and propose a well-constructed way of these OOV tokens. By clustering the learned representations from historical user-item interactions, we make the representations of user/item combinations share the same OOV tokens if they have similar properties. This construction allows us to capture user/item relationships well (memorization) and preserve the diversity of descriptions of users and items (diversity). Furthermore, integrating these OOV tokens into the LLM’s vocabulary allows for better distinction between users and items and enhanced capture of user-item relationships during fine-tuning on downstream tasks. Our proposed framework outperforms existing state-of-the-art methods across various downstream recommendation tasks.

Improving LLMs for Recommendation with Out-Of-Vocabulayy Tokens
Ting-Ji Huang 12
Jia-Qi Yang 12
Chunxu Shen
Kai-Qi Liu De-Chuan Zhan 12 Han-Jia Ye 1.2
Abstract
Instraction Nocaaiuuorry Tokens should
ny DEE
Characterizing users and items through grouped vector representations is crucial for various tasks in recommender systems. Recent approaches attempt to apply Large Language Models (LLMs) in recommendation through an instruction-based format, where real items are represented with grouped words formed from in-vocabulary tokens. However, these tokens do not effectively repre- sent items since their meanings are derived from pre-training on natural language tasks, which limits the model's ' ability to learn meaningful user-item relationships. this paper, we explore how to effectively characterize users and items in LLLM-based recommender systems from the tokenizing strategy view. We demonstrate the necessity of using out-of-vocabulary (OOV) tokens for the characterization of items and users, and propose a well-constructed way of these OOV tokens. By clustering the Iearned representations from historical user-item interactions, we make the representations of user/item combinations share the same OOV tokens if they have similar properties. This construction allows us to capture user/item relationships well (memorization) and preserve the diversity of descriptions of users and items (diversity). Furthermore, integrating these OOV tokens into the LLM's vocabulary allows for better distinction between users and items and en- hanced capture of user-item relationships during fine-tuning on downstream tasks. Our proposed framework outperforms existing state-of-the -frte- methods across various downstream recom- mendation tasks. Code is available at httpss //github. ctt.uuu..coominigii22/i META ID
Ansuer(Si): Wi should reammedd EUT LURD
Meproerntaroo
Nerwok 1D
Instraction
(Slmn))
Question: Considering TAE
has bought
[n E0ll B000
what should we tecommend for hmm?
Arswer/ME (D): should
rocomimen (imm
Commrnated tnd
MIRTA ID (huen)
Grozad Truth
Figure 1: Left: Comparison of tokenizing strategies using In- V--abbublary (IV) and Out-of- Vocabulary (OOV) tokens to characterize items. The IV scenario leverages numeric identifiers (Numeric ID) for items, while the OOV scenario constructs OOV tokens from user-item interaction history (META ID). Right: Similarity heatmaps illustrate the align- ment of Numeric ID, META ID, and ground truth, demon- strating the effectiveness of META ID in capturing relation- ship patterns. Ground truth is calculated from Equation
1. Introduction
Modern recommender systems (RS) play a crucial role in various applications like video recommendation (Davidson et al. 2010). e-commerce (Chen al. 2019). and social networking (Fan al.] 2019). The recent advent of
large language models (LLMs) offers new direction of exploration in this realm. Models such as T5 (Raffel et al. 2020a) and LLaMA (Touvron et al. 2023), training on massive natural language data, achieve impressive language understanding capabilities text generation and
conversation tasks (Zhao al. (023). Their success drives
explorations to use pre-trained LLMS model backbones for handling various recommendation tasks like sequential recommendation, rating, and explanation task (Bi al. 2022 Wu et al.] 2021). In such frameworks, all input features including ids, attributes, and contextual signals, are described as textual queries, and the LLM is expected to generate task-specific labels as responses (Cui al. 2022 Geng al. 2022 Zhai et al. 2024 Hong et al. 2025 After being trained in this instruction-bann
'School of Artificial Intelligence, Nanjing University "National Key Laboratory for Novel Software Technology, Nanjing Uni- versity "WeChat Technical Architecture Department, Tencent Inc. "Software Institute, Nanjing University. Correspondence to: Han- Jia Ye <ychj@ @ lamda.nju.ce aanda.nju.edu.nn>.
format, the model can be prompted to provide recommendations for user generate explanations for those recommendations.
Proceedings of the nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
How to characterize users and items so that LLMs can learn their personalized information during fine-tuning? Given that tokens are the fundamental units of input

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Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
for LLMs, where each represents the smallest semantic element the model can process, it is natural to allocate specific tokens to represent users and items, allowing the model to learn their characteristics through token-level associations. While early works (Cui et al., 2022) directly represent a real item by text tokens (c.g., "blue", "casual", "T-shirt"), LLMs might answer text that does not correspond to a real existing item (Hua et al., 2023 Ji et al. 2023).
align the token embedding layer of LLMs with linear trans- formation layer to enrich the OOV token representations as an augmentation. Our contributions are as follows:
We introduce memorization and diversity scores to evalu- ate ID representations in LLM-based recommender sys- tems, focusing on capturing user/item correlations and ensuring diversity.
Recent advancements explore representing users/items with
We develop META ID, which uses out vocabulaay to- kens to characterize users/items, enhancing the memoriza- tion and diversity of their representations for LLMs.
Identifiers (IDs) (Geng et al. 2022 Wang et al. 2022 Gao
et al. 2023 Bao et al. 2023). p. As illustrated in Figure
real item (Item No. 20240 can be indexed with a compound
The experiments show that META ID improves memo- rization and diversity score, which leads to improvements in various recommendation tasks.
word combined with several in in-vocabulary tokens ("item",
"20", "24"), and we call such a token combination as an ID. Each ID uniquely identifies real item.
2. Related Work
Characterizing users and items with in- in-vocabulary tokens is straightforward but also introduces the mupping problem, where it lacks diversity to align limited in-vocabulary to- kens to billions of items in recommender systems, and token combinations are prone to lead to language conflicts (Bao jet al. |023 Hua et al. 2023). As shown in Figure [ using numeric tokens to represent items leads to similar represen- tations of distinctive items. One solution is to create brand new tokens (OOV tokens) specifically allocated for IDs to allow distinction, as in the previous work on Collaborative ID (CID) (Hua et a 2023). However, we need to train these new token embeddings from scratch, which often requires a large amount of data, and our experiments also indicate that it is hard for LLM to capture the collaborative corre- lations of users/items during fine-tuning (see Section 4 Therefore, we should utilize redefined OOV tokens capable of capturing user/item correlations (memorization) while also distinguishing different items (diversity), two dimen- sions that are positively correlated for the performance of recommendation tasks.
Characterizing Items by IDs. Modern recommendation models usually use unique IDs to represent users and items, which are subsequently converted to embedding vectors as learnable parameters (Fan et al. 2023). Common approaches include matrix factorization (Koren et al. 2009a Rendle et al. 2009). two-tower models (Wang et al.
2021 and deep neural networks (Sun et al. 2019 Kang
and McAuley 2018 /Zhou et al., 20200) Zhang et al. 2023b
Xie et al. 2022 Wang et al. 2023). which make predictions
by examining historical user-item interactions to identify behavioral patterns and enable collaborative recommenda- tions (Koren et al.) [022]. To avoid large-scale vocabularies, one recent study proposes using hierarchical sequential transduction units to handle non-stationary vocabulary (Zhai ct al. 2024). While some recent approaches adopt the con- cept of ID to represent the token combinations that character- ize items in LLMs (Geng et al., 2022 Hua al., 2023). We follow previous studies and also call these token combina- tions as ID. This paper aims to combine LLM with ID more efficiently by proposing a new ID construction method.
In this paper. we present META ID (META METAA-aath-uuidd Ienntifee), framework for characterizing users/items using out-of-vocabulary (OOV) tokens for LLM-based recommendations. Initially, we generate meta-paths to represent user-item interactions and then obtain user and item representations from a skip-gram model trained on these meta-paths. A meta-path is a sequence that represents interactions between users and items in a graph structure. By clustering these meta-pathh-basd representations, we create hierarchical groups that serve as OOV tokens for con- structing user and item IDs. This approach extends beyond previous research, which has predominantly focused on item IDs (Geng et al., 2022) Rajput et al., 2003), by making the representations of users/item IDs share the same OOV tokens if they have similar properties. Finally, integrating these OOV tokens into the LLM's vocabulary allows for bet- ter diversity and enhanced memorization during fine-tume tuning on downstream recommendation tasks. Additionally, we
Instruction Tuning for Recommendation. The integration of Large Language Models (LLMs) into diverse tasks has seen significant growth recently. Recent works fine-tuned pretrained language models on large-scale NI.P datasets verbalized via human-readable prompts (Sanh et al. 2022 Wei et al.) (022). These instruction tuning methods design prompts containing detailed task descriptions and adhere more to the natural language format. Driven by their exceptional natural language processing capabilities, researchers aim to transfer their linguistic ability to enhance recommender systems (Li et al.. 2021 Zhang et al., [023c] Hong et al. 2025). These LLMs process user interactions as sequences of tokens and, through fine-tuning, predict users' future interests based on past activities (Zhang et al. 1021 Chen 2023). Moreover, some studies reframe tasks like retrieval, rating, and explanation generation as language comprehension tasks (Cui et al. 2022 Geng et al. 2022
2

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Improving LLMs for Recommendation with Out-Ot-Vocabulary Tokens
Li al. 2023b), allowing LLMs function as multi-task recommenders, producing recommendations and explana- tions with unified architecture. In this paper, we apply LLMs based on instruction tuning of multi-task scenarios.
tokens, formulated as:
[ Σ log (m yea, B(x)), (1)
0' arg min Lo =
0
ID Construction with Tokens. Recent studies explore using in- tn-vocabulary tokens to characterize items in LLMs, where users and items are represented by token combina- tions called IDs. Early efforts such as P5 (Geng al., (022) convert user-item interactions into natural language formats using numeric IDs constructed of in-vocabulary tokens of the T5 model (Raffel et al. 2020a). Further, sequential ID (SID) and collaborative ID (CID) are developed to enhance item information sharing (Hua et al. 2023). Some recent study explores the traditional II) embeddings with LLMs RRajput et al., 2023 Li al., 2023a Zhang et al. 2023a Despite these advancements in ID construction, challenges remain in the lack of ID construction criteria and the focus on item IDs. In contrast, our META ID approach assigns memorization and diversity scores for evaluation and constructs both user and item IDs, which improves the characterization of users and items.
where O represents the optimal set of parameters that we aim to learn. This supervised learning approach helps the model to internalize personalized information about users and items through the token embeddings of their respective IDs, D. and x
3.2. Represent Users/Items with In- In-Vocabulary Tokens
The token embedding layer E in LLMs transforms input tokens into corresponding token embeddings. Since users and items are also represented by IDs constructed of tokens, the corresponding token embeddings of ID V. and æ; are the key to assess how L.LMs capture and represent different items. We define the ID representation e; for an item ID æ, as the average embeddings of its token combinations:
e,
Σ Zi. Ex.
E E(si),
(2)
3. Preliminary
where E(x,,) denotes the embedding vector of a token Ti. For example, an item identified by "item 2024" is repre- sented by the average embedding vectors of three tokens ("item", "20", "24").
We describe recommendation tasks (e.g., sequential recom- mendation) under the instruction tuning setting, in which all data such as user-item interactions are converted to natu- ral language sequences in a question&answer format (e.g., Input: Please recommend an item for user 2024 consider he has purchased item 2023; Output: item 2024). More
4. Metric for Representation Evaluation
examples presented in Appendix C
As mentioned above, using numeric IDs to represent items leads to similar representations of distinctive items. For an intuitive explanation, we first visualize the cosine similarity matrix between items using heatmaps in Figure D where Random ID (RID) means assigning each item with random number. It shows that RID and SID result in a large number of similar items due to semantic conflicts, and META ID constructed of OOV tokens shows distinguished similarity closer to the ground truth. We further plot the ID represen- tations using T-SNE visualization in Figure 2b It is clear that RID and SID (using in in-vocabulary tokens) shrink in small place relative to CID (using OOV tokens), reflecting in in-vocabulary tokens lacking expressive power for distinc- tive users and items. To quantify this, here we introduce two metrics to assess the memorization and diversity of ID representations.
3.1. Instruction Tuning for Recommendation
Given a user set U, an item set I and token set T from LLM's vocabulary, we formulate a recommendation task as natural language instruction that pairs sequence of n input tokens x = (21, ..... .nn with a sequence of m corre- sponding label tokens y = (y1,-., ). The goal is to train and LLM MM to generate y given x. For simplification, each token in the input or output sequence is denoted as w; or yy. 34, 8/ E T, respectively. user E U is represented by a set of pre-assigned tokens x. :uggs -.. Simi- larly, item i E I is represented by sequence of tokens x;- = (I;,, .)..
The LLM Mo employs a token embedding layer E()) with parameters Og E 0, functioning as a lookup table that transforms each input token into a token embedding. It subsequently predicts the probability distribution of label tokens by forward propagation. The training objective is to minimize the negative log- likelihoeoood of the label tokens conditioned on the input sequence and previously generated
Diversity Score (DS) is a metric designed quantify the diversity of ID representations within LLMs. Items repre- sented by ID representations should be easily distinguish- able for the model. For a pair of items and j, their dif- ferentiation can be calculated using the Kullback-Leible (KL) divergence on ID representations e; E E and e, E E from Equation 2 which reflects how distinct they are the
3

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Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
1.00
CID
0.7
0.01
2050
METAID ID
0.03
0.00
0/9
0.22
510
Tokens
Tokees
ND00810
0.75
0.72
0.50
0.4
Sports Beauty Toys
0.0
0.25
0.00
Beauty
Toys
CID All Tokeess
META ID O All Tokens
0.00
0.2
0.6
MS+DS
14
(a) Comparison of DS and MS.
(b) T-SNE of token representations.
(c) (MS+DS) with Performance
Figure 2: (a) Comparison of Diversity Score (DS) and Memorization Score (MS) of ID representations from RID, SID, CID and METА ID; (b) T-SNE visualizations of ID representations on Sports dataset; (c) Correlation between (DS MS) with NDCG@ @ on sequential recommendation task.
model's embedding space:
score unsatisfactory. These findings highlight the need further develop more effective forms of OOV tokens for ID construction, aiming to improve LLMs' ability to capture user/item correlations and distinguish items.
ES(
DKz(eellez) + PRz(e)lla) (3)
2N
N..JEI
Here, the [eT [eT ......C.. .el means the embedding matrix of all items i E I. N represents the number of randomly selected item pairs, where we apply sampling strategy to reduce the computational complexity, and we give conver- gence analysis in Figure 5
5. META ID
We now introduce the META ID framework enhance LLMs in recommendation tasks. META ID involves creat- ing out-of-vocabulle (OOV) tokens for constructing user and item IDs, which provide rich, expressive space and encapsulate comprehensive, collaborative information. As illustrated in Figure 3 our process begins with sampling meta meta-paths from user-item interaction history. We then ap- ply a skip-gram model to learn user and item representations from these meta-path sequences. This process ensures that users' and items' features projected into shared space to capture their interaction relationship better. This is fol- lowed by K-Means clustering group similar users items, after which unique OOV tokens are assigned to each cluster to construct the META ID.
Score (MS) quantifies the relationships cap- tured by ID representations through measuring the similarity between items and users. We use the adjusted cosine simi- larity formula (Sarwar al. 2001), to provides ground truth relational values between users and items, which are given by:
Memorization
sim(i,5) Eneu (Ru, Ru)) ) (Ros - Raa)
Emee (Bu,i - R2)2. Eney (Rus R2, (4)
where Ry,, and Ru, denote user u's ratings for items i and and R, is user u's average ratings. To assess the rela- tionship captured by learned ID representations, we employ Mean Square Error (MSE) to calculate the similarity bias on their cosine similarity with their corresponding ground truth relation values, which forms the basis of the MS:
5.1. Meta-path-bass Embedding
We frame user-item interactions within graph embedding learning paradigm (Dong et al., 2017), constructing an in- teraction graph composed of user nodes and item nodes I, linked by interaction history with ratings R. The core of this embedding learning strategy involves meta-paths (Dong) et al. 2017) sequence of connections that reflect com- posite relationships within the graph. Our primary meta- path, denoted as U-I-U, materializes when users consistently rate an item with the same score r. forming a path:
2 MS( [| 1)
Σ IJENNCC
c. [eelle//
sim((..)) (5)
Our quantitative assessment, based the metrics intro- duced, reveals intriguing insights into the quality of these ID representations. Figure Sasureealillueeee illlustrates that IDs constructed from in-vocabulary tokens (RID and SID) perform poorly in terms both diversity and memorization. Although the use of OOV tokens (CID) enhances diversity, its memorization
p {U I Ln U3 4 I RR
(6)
We employ a skip gram model to capture the interaction dynamics represented by these meta-paths. This model

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Improving LLMs for Recommendation with Out-Or-Vocabulary Tokens
Unerdien Interaction
Meda Paths
Output:
Ammo7700-4t
de
LLM (e.g.
78
Token Emiledding Layer
LLM's Vocabulary
User-Iacm Raps.
Dveesliom Space
In-Vecabalary Tokeas
Linar Transformation
Cnter
Tokens:
jur
Sauple
Aeoo
Iapet: useetTidodo her drughh (onecTifac112,, nou
META ID construction for users and items.
(b) Integration of META ID for sequential recommendation.
Figure 3: Illustration of the proposed framework: (a) The first step involves sampling meta-paths to train skip-gram model to learn the representation of users and items. Following this, users and items are represented by META IDs constructed of OOV token combinations through clustering their representations. (b) A large language model (LLM) integrated with META ID is utilized for sequential recommendation. The LLM first encodes the input sequence into tokens and then lookup their embeddings through the token embedding layer. Here, the OOV tokens undergo an extra transformation layer before the token embedding layer of LLMs as a representation augmentation.
trained on sequences generated from meta-path-bas based random walks (Dong et al., 2017), producing represen- tations WU [w., [Ways.., L [dx[U] and Wi
centroid. In detail, we assign fine- -ra-ggraiddd tokens "(CI))" in ascending order according their distance from the cluster centroid, and use
fine-grained token.
[W] w T im E _dxlil, denoted the representations for [U] users and [ items in d-dimension space respectively.
The resulting identifiers, META ID, combine coarse- grained token and a fineggraied one that uniquely identifies each user or item within that cluster. For example, an item might be represented as "(Ite) (CT:) (CI,)" labeled with three tokens, "(Item)", "(CTY", "(C4)" where "(Item)" denotes it as item, "(CT,)" is its coarse-grained token and "(CT)) is its fine-grained token.
5.2. OOV Token Generation
To balance diversity and memorization in large-scale rec- ommendation systems, we adopt cluster-based approach. Representations derived from user-item interactions are or- ganized into shared embedding space and segmented into G' clusters. Each cluster center, Hg. is defined by the average of representations within that cluster, effectively capturing the collective characteristics of its members. These cen- troids then categorize each user and item, providing a refined foundation for constructing granular IDs. The generation process is two-step procedure:
This clustering and labeling process effectively compresses the vocabulary needed for ID representation while preserv- ing the rich information necessary for recommendation tasks. In our implementation, we apply K-Means cluster- ing (Arthur and Vassilvitskii 2007), utilizing cosine sim- ilarity to measure the affinity of representations cluster centers. This method simplifies the complexity of represen- tations space management and proves to be robust in our experimental validations of Section 6.4
1). Assign coarse-grained tokens based on centroids. We first cluster the learned representations W into G clusters. For each cluster g. let Ig = {i 9: = g} denote the index set of representations assigned to it. The centroid y is then computed as:
5.3. Integration of META ID with LLMs
The integration of OOV tokens of META ID, denoted as Xoov, with LLMs involves expanding the vocabularies. This is achieved by extending the token embedding layer's param- eters from Ag R|T|xd to g TR (T(([++))x where [ the number of in-vocabulary tokens, is the number of OOV tokens, and d is the dimension of the token embeddings.
W;
g=1
As coarse-grained distinction between users and items, we assign an OOV token "(CT;)" to each centroid.
A good initialization helps token embedding learning, here we give representation augmentation approach different
2). Assign fine-grained tokens based on distance. Within each cluster, assign a token based on the distance to the
5

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Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
Table 1: Performance comparison of different methods on sequential recommendation task. META ID (T) and META ID (L) refer to the use of T5 and LLaMA2-7b as the backbone. The best and second-best performance methods are denoted in bold and underlined fonts respectively.
Sports
Beauty
Toys
Methods
H@5
N@5
H@ 10
N@ 10
H@5
N@5
H@
10
N@ 10
H@5
N@5
H@ 10
N@ 10
Caser Tang and Wang
2018
0.0116 0,0072 0.0194 0.0097 0.0205 0.0131 0.0347 0.0176 0.0166 0.0107 0.0270 0.0141
HGN Ma et al.] 2019
0.0189 0.0120 0.0313 0.0159 0.0325 0.0206 0.0512 0.0266 0.0321
0.0221
0.0497 0.0277
GRU4Rec (Fidasi et al.
2016
0.0129 0.0086 0.0204 0.0110 0.0164 0.0099 0.0283 0.0137 0.0097 0.0089 0.0176 0.0084
BERT4Rec (Sun et al. 2019
0.0115 0.0075 0.0191 0.0099 0.0203 0.0124 0.0347 0.0170 0.0116 0.0071 0.0203 0.0099
FDSA Hao et al._ 2023
0.0182 0.0122 0.0288 0.0156 0.0267 0.0163 0.0407 0.0208
0.0228 0.0140 0.0381 0.0189
SASRec Kang und McAulcy
2018
0.0233 0.0154 0.0350 0.0192 0.0387 0.0249 0.0605 0.0318 0.0463
0.0306 0.0675 0.0374
R* Zhou ct al. 2020
0.0251 0.0161 0,0385 0.0204 0.0387 0.0244 0.0647 0.0327 0.0443
0.0294
0.0700 0.0376
CL4SRec (Xre et al.
2022
0.0219 0.0138 0.0358 0.0182 0.0300 0.0201 0.0546 0.0270 0.0427 0.0244
0.0617 0.0305
TIGER (Rajput et al. 2023
0.0264 0.0181 0.0400 0.0225 0.0454
0.0320
0.0648
0.0384
0.0521
0.0371
0.0712 0.0412
RID Hua et al. 2023
0.0208 0,0122 0,0288 0.0153
0.0213
0.0178 0.0479
0.0277
0.0044
0.0029
0.0062 0.0035
SID Geng et al. 2022
0.0223 0.0173 0.0294 0.0196 0.0404 0.0299 0.0573 0.0354
0.0050
0.0031
0.0088 0.0043
CID
Hua et al. 202.3
0.0269 0.0196 0.0378 0.0231 0.0336
0.0227 0.0507
0.0281
0.0172
0.0109 0.0279 0.0143
META (T)
0.0322 0.0223
0.0487
0.0277
0.0510
0.0351 0.0753
0.0429
0.0533
0.0372 0.0761 0.0441
META ID (L)
0.0392
0.0278
0.0561
0.0332 0.0458 0.0320 0.0678 0.0391 0.0387
0.0264 0.0535 0.0312
from previous works using random initialization (Geng et al.
dataset (Ni et al. 2019). focusing specifically on Sports, Beauty, and Toys. The datasets are processed following the methodology in P5 (Geng et al. 2022).
2022, Hua al. 2023). As shown in Figure (3b] the OOV to- kens undergo linear layer F--) initialized with the category embeddings Hlg from Equation / Finally, the training objec- tive for integrating META ID into LLMs is reformulated as:
Baselines. We compare to variety of established mod-
els (which are described briefly in Appendix spanning
0" arg min Co (0))F)
from CNN-based to LLM-based frameworks. Caser (Tang
and Wang
2018).
HGN (Ma et al. 2219). GRU4Rec (Hi-
ly|
dasi et al.
2016).
BERT4Rec (Sun et al., 2019). FDSA
_ log Pe (y yet, E' F(xoov)),
Hao et al.
2023).
SASRec (Kang and McAuley 2018
SC (Zhou al.
2020). CL4SRec (Xie et al.) (022) and
TIGER (Rajput al. 2023). Specifically, we provide P5
where 0' now includes F, aligning with our modified emhedding layer gg to optimize the model's performance with the OOV tokens Xoov. This training goal ensures the model can effectively distinguish between diverse users and items, improving its ability to capture user-item relationships. By enhancing memorization and diversity of OOV tokens, the model achieves better performance in recommendation tasks, leading to more accurate and personalized recommendations in our experiments.
with its variations, equipped with different ID) construction
strategies like Sequential ID (SID), Random ID (RID), and Collaborative ID (CID) (Hua et al. 2023).
Evaluations. We apply widely accepted metrics, top-k Hit ratio @H@ K) and Normalized discounted cumulative gain (N@ @ K) with K 5, 10 to evaluate the recommendation performance.
Implementation Details. For constructing META IDs, the clustering groups are limited to |G|=100 (200 for Toys). For LLM fine-tuning, we consider both encoder-decoder architecture T5-small (Raffel et al.] (2226) and decoder- only architecture LLaMA2-7b ( (Touvron et al., 2023). We fully fine-tune the T5 model and employ the LoRÁ (Hu) et al. 2022) to fine-tune LLaMA2-7b. Vocabulary sizes of these models are shown in Table 10 For LLM inferencing, we use beam search to generate potential items evaluated under the all-item setting.
6. Experiment
We evaluate META ID on five downstream recommendation tasks: sequential recommendation, direct recommendation, rating prediction, explanation generation, and review related tasks. We analyze the influence of critical components in META ID and assess the ID representations through visualization and our proposed metrics. Details of task descriptions and pre-processing are in Appendix C
Results Table D presents our findings for sequential rec- ommendation task Our observations are as follows: 1)
6.1. Evaluation on Sequential Recommendation
Setups. We evaluate our META ID framework on three pub- lic real-worke datasets from the Amazon Product Reviews
We show the standard error of the metrics in Table 12
6

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Improving LLMs for Recommendation with Out-Or-Vocabulary Tokens
Table 2: Performance comparison of different methods on direct recommendation task.
Sports
Beauty
Toys
Methods
H@5
N@5
H@
N@ 10
H@5
N@5
H@10
N@
H@5
N@5
H@
N@ 10
RID
Hua et
2023)
0.0030 0.0023 0.0042 0.0027
0.0203
0.0155
0.0276
0.0178
0.0046
0.0030
0.0063 0.0035
SID Geng ct al.
2022
0.0211
0.0169 0.0267 0.0187 0.0296
0.0226 0.0405 0.0261
0.0025
0.0014 0.0041 0.0019
CTD Hua 2023)
0.0250
0.0189 0.0342 0.0219 0.0216
0.0147 0.0340 0.0187 0.0076 0.0049 0.0014 0.0070
META ID
0.0357 0.0256 0.0520 0.0308 0.0480 0.0336 0.0689 0.0403 0.0564 0.0391 0.0803 0.0468
Table 3:
Performance comparison of different methods on rating prediction, explanation generation, and review tasks.
Sports
Beauty
Task Type
Metric
RID
SID
CID
META ID
RID
SID
CID
META ID
Rating
RMSE
1.0382
1.0486
1.0383
1.0327
1,2829
1.3098
1,2819
1.2818
BLEU-1
16,2567
16.5825
16.6121
16,9005
18,2299
18.3981
19.3499
19,5106
Explanation
BLEU-4
2.1782
2.1944
2.2332
2.3481
2.9027
2,8071
3,0626
3.0592
BLEU-1 7.6140
7.7948
7.6586
7.8819
6,2282
6,5055
6,5854
7.0500
Review
BLEU-4
2.3228
1,2406
2.4109
2.6546
1.9891
1,2406
1.9718
2.7485
META ID demonstrates superior performance on all three datasets, underscoring its robustness. 2) IDs constructed of in- ve-vocbbulary tokens, RID and SID, underperform on Toys, suggesting limitations in their recommendation efficacy for LLMs. 3) CID shows marked improvements over RID and SID on Toys dataset, highlighting the benefits of incorporat- ing OOV tokens with collaborative information. 4). While the LLaMA2-7b backbone is better in the Sports dataset, its performance in the Beauty and Toys dataset is not as good as T5, which could be linked to the distinct fine-tuning methodologies applied to these models.
three tasks in Table 3 show that META ID significantly improves the BLEU scores Sports and Beauty compared to other methods. This result suggests that META ID can improve performance in text relevance tasks, including the interpretation of recommendations.
6.3. Evaluation of ID) Representation
Visualization. The amount of numeric tokens available in LLMs is relatively limited, which complicates the establishment of unique one-to-one ID relationships, and two unrelated items might share the tokens as ID. For an intuitive explanation, we visualized the cosine similarity matrix between items using heatmaps in Figure TT where random sample 50 items from the Toys dataset and take their adjusted cosine similarity from Equation 4 as ground truth compared with RID, SID and META ID. RID and SID result in a large number of similar items due to semantic confficts, while META ID shows distinguished similarity closer to ground truth. This suggests that using META ID allows LLMs to better capture relationships between users and items.
6.2. Evaluation Various Recommendation Tasks
Setups. To validate META ID's adaptability, extend our evaluation to include direct recommendation, rating prediction, explanation generation, and review tasks, akin to P5 (Geng et al. 2022). For direct recommendation, the model is asked to recommend item for users directly without providing user's interaction history. For rating prediction, the model predicts a numerical rating between I and based on user-item data. For explanation tasks, it generates textual justifications for a user's preference towards an item, while in review tasks, it summarizes lengthy reviews into concise titles.
Quantitative Analysis. We quantitatively assess the quality of these ID representations by the proposed two metrics (Section 4 memorization score (MS) and diversity score (DS). Our results, shown in Figure 2a indicate that con- structing IDs of in-vocabulary tokens (RID and SID) per- form poorly in the diversity dimension. For intuitive inter- pretation, further employ t-SNE visualization to map ID representations and observe tendency for these tokens to cluster narrowly in Figure 26 META ID shows robust mem- orization and diversity across three datasets, reflecting its ability to capture correlations between users and items from historical data while ensuring items remain distinguishable.
Evaluations. For direct recommendation, we apply the same metrics as in Section 6.1 I For rating prediction, we use MSE metric. For explanation and review tasks, we employ BLEU-1/4 1/4 metrics.
For direct recommendation (Table 2) META ID exceeds other methods across datasets in all-item setting. This suggests that META ID effectively model the direct re- lationship between users and items. The results for the other
Results.
7

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Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
Table 4: Performance of different construciton methods for OOV tokens on sequential recommendation task.
Sports
Beauty
Toys
Methods
H@5
N@5
H@ 10
N@
H@5
N@5
H@ 10 N@
H@5
N@5
H@ 10
N@ 10
DRSCAN Ester et al.
1996
0.0078 0.0043 0.0145 0.0065 0.0257 0.0168 0.0428 0.0223 0.0180
0.0109 0.0304
0.0149
Spectral
luger al. 2003
0.0199 0.0124 0.0336 0.0167 0.0360 0.0236 0.0588 0.0310 0.0295 0.0184 0.0514
0.0254
RQ-VAE
Zeghidour al 2022
0.0122
0.0077 0.0171 0.0093 0.0368 0.0254 0.0536 0.0309 0.0511 0.0335 0.0667 0.0395
K-Means Arthur and Vassi Vilsk1 Vissivilskm
2007
0.0322 0.0223 0.0487 0.0277
0.0510
0.0351 0.0753 0.0429 0.0503 0,0352 0.0742 0.0429
2003
against K-Means clustering (Arthur and Vassilvit
0.040
Beauty
skii
2007) We also consider generating OOV tokens with RAE Zeghidour et al. 2022) using meta-path-based em- beddings. Our results show that simply applying K-Means outperforms other grouping methods in most cases.
Sports
0.035
Toys
NDCG@
0.030
0.025
0.020
Embedding Extraction. To assess the necessity of the meta- path approach, we perform ablation study where the meta- path structure is removed, and the embeddings are learned purely based on user-item interactions without higher-order relationships with Matrix Factorization (MF) (Koren et al., 2009b). We also compare our approach with more recent graph-based models GCN (Kipf and Welling 2017). which are designed to capture higher-order relationships graph- structured data. As shown in Table [5] the meta-path- based embedding used in our approach outperforms than oth- ers. This is due to our training on user-item interaction sequences, which allows it to better capture temporal dy- namics and patterns user behavior.
0.015
0.010
2,000
4,000
6,000
8,000
10,000
Size of OOV Tokens
Figure 4: Performance comparison of different OOV token size within META ID on three datasets.
Table 5: Performance comparison of different embedding extraction strategies on sequential recommendation task.
Beauty
Embedding Type H@5
N@5
H@ 10 N@10 10
OOV Token Size. Since varying cluster sizes result in different numbers of OOV tokens, we also investigate the impact of different cluster sizes for META ID in Figure 4 We find that the granularity of token clusters plays crucial role in recommendation performance. An excessive token scale can introduce noise, reducing the performance. There- fore, finding an optimal token size is vital to ensure that META ID effectively adapts to various datasets' nuances.
MF-baaed 0.0220 0.0142 0.0344 0.0207
GCN-Based 0.0267 0.0198 0.0382 0.0212
Meta- Basa-
0.0322 0.0223 0.0487 0.0277
Metries Analysis. Furthermore, we conduct correlation analysis to explore the relationship between MS/DS and sequential recommendation performance. We sum the MS and DS of different ID strategies and plot their performance on the sequential recommendation task. As shown in Figure 2c, the sum of the MS and DS positively correlate with NDCG@ 10, suggesting that memorization and diversity of IDs are two essential properties in recommendation tasks.
7. Conclusion
This study introduces META ID, a method enhancing Large Language Models (LLMs) for recommender systems us- ing OOV tokens. Moving beyond constructing IDs with in-vocabulary tokens, META ID incorporates user-item in- teraction information align LLMs more effectively with recommendation tasks. We learn representations from user- item interactions utilizing meta-path patas sampling. By clus- tering these representations we generate OOV tokens to construct META ID. This approach guarantees tokens cap- turing correlations between users and items from historical data while ensuring distinctiveness among item. Our ex- periments across various real-world datasets demonstrate META ID's robust performance in diverse recommendation tasks, including sequential and direct recommendation, as well as complex tasks requiring detailed textual responses.
6.4. Ablation Studies
We analyze the properties of META ID following the eval- uation in Section 6.1 including the impact of grouping methods, the size of OOV tokens, and different indexing ranges on the performance of META ID.
Token Grouping. We study the importance of different grouping methods for OOV token generation in our frame- work. In Table 4 we compare the performance of DBSCAN (Este et al. 1996) and Spectral Clustering (Kluger et al.
8

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Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
Essentially, META ID effectively combines the capabilities of L.MMs with the nuanced requirements of recommendation scenarios, such as planning highly personalized content for users as virtual shopping assistants.
Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In KDD,
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Yihong Eric Zhao, Jiliang Tang. and Dawei Yin. 2019. Graph Neural Net- works for Social Recommendation, In wwW.
Acknowledgements
This work is partially supported by National Key R&D Program of China (2024YE00202800), NSFC (623761 18, 62476123), CCF-ALIMAMA TECH Kangaroo Fund NN0.2024005), Key Program of Jiangsu Science Foundation (BK20243012), Fundamental Research Funds for the Cen- tral Universities (2024300373), Collaborative Innovation Center of Novel Software Technology and Industrialization.
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023, Rec- ommender Systems in the Era of Large Language Models (LLMs). CoRR (2023).
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023. Chat-REC: Towards Inter- active and Explainable LLMs-Augmented Recommender System. CoRR (2023).
Impact Statement
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. Recommendation as Lan- guage Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). In RecSys.
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Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
Appendix
T4 E No:
We provide details omitted in the main paper.
arg max ΣE NEV TEU, n,EN,
Σ
log P(nile)
(9)
Appendix A Workflow of META ID, encompassing the construction of OOV tokens.
and the P(n (u) is calculated as:
Appendix Details of memorization score (MS) and diversity score (DS).
exp(wn we) P(n/(o) = EjEv exp( w3 w.))
(10)
Appendix C Experimental setups and implementation details of META ID.
where w means the representation of one user or item. In experiments, we set number of negative sampling to 5, and train the skip-gram model for 10 epoch with learning rate of 0.001.
Appendix D
Additional experimental result analysis.
Appendix E.
Discussions and limitations of META ID.
A.2. How to generate OOV tokens
A. Details of META ID
This section complements subsection 5.2 where we gener- ate OOV tokens from users and items representations for constructing META ID. Essentially, we need build hier- archical classification system for IDs in order to express a wider range of items and users with as few OOV tokens as possible, so that similar items and users are under the same hierarchical branch.
In the Section of the main text, we elucidate the compre- hensive workflow for generating META ID, This process encompasses three main steps, including (1) the extraction of meta-path -ased embedding. (2) the generation of OOV tokens, and (3) the incorporation of META ID with LLMs, thereby handling with various downstream recommendation tasks.
This hierarchical construction mechanism very reminis- cent of clustering methods, we apply in META ID, in which
A.1. How to extract the meta-path-based embedding
out-of-class indexes and in-class indexes as two levels of IDs. Though more sophisticated clustering method for multi-levels structure can be applied, in ex- periments, we use the simple K-Means clustering, which is more suitable for large-scale data volume due to its sim- ple and easy to optimize nature. We also demonstrate the effectiveness of this approach in Table
This section supplements the details of subsection 5. i.e., the users / items representations extracted from a skip-gram model, including the sampling process of meta-paths as training data.
In META ID, enable a skip- -raa model to learn effective users items representations from the sampled meta-paths, which is learning user representations Wo and item rep- resentations W,. The objective of the skip-gram model learning paradigm is to map the users and items in the meta-paths seqeuences into a diver-dimensional space as in (Dong et al. 2017)
In experiments, cluster user and item representations together. We then create the between-cluster tokens (CT,) for cluster i, and sort the in-cluster users / iitems based on the cosine distance to the cluster centroids to get in- cluster tokens (y:). Finally, we add a prefix token (Item) or (User) to denote its type. For example, an item might be represented ((tem) (CT,) (i)" labeled with three tokens ("(Item)", "(CT,)" "())", where "(Item)" denotes it an item, "(CT,)" is its cluster token, and "(y:)" )" is its in-cluster token.
Meta-paths sampling. Firstly, constructs node se- quence based on random walks of meta-paths. A meta-path p - P1 Ry P2 n2 RE-1, Pe is path that is defined on a graph, where R2 signifies a composite relation between different node P. We define user-item-user (U-I-U) as our meta-path, where paths only exist if users has given the same ratings R₁ to one item. We sample 32 rounds starting from each user and item with the sampled length & 64.
It is worth noting that a naive approach to generating user and item IDs is to assign an independent OOV token as ID (IID) that needs to be learned for each item and user. However, this not applicable to modern recommender systems with large number of items and users, as the training time may be too long if large number of new tokens need to be created. We also show it in Table g where we use the meta-path peeta-path-basedd based embeddings with a linear projection layer for initialization, which shows that it is ineffective compared to META ID.
Skip-gram model training. In the second step. through sampling meta-paths based on random walks as training cor- pus, we train skip-gram model thus learn the vector repre- sentations (Wo, W,) for all users and items. The objective of the skip-gram model is to maximize the conditional prob- ability P(n.(o) v)) for the node e E V of its neighboring node
12

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Improving LLMs for Recommendation with Out-Ot-Vocabulary Tokens
Table 6: Examples of prompts for recommendation tasks (Part 1).
Sequential Recommendation
Direct Recommendation
Rating Prediction
Task Input:
Considering user 2024 has interacted with items item 1, item 2. What is the next recommendation for the user?
What should we recommend for user 2024?
Which star rating will user 2024 give to item item 2? (1 being the lowest and 5 being the highest).
Task Output: item 2024
item 2024
5
Table 7: Examples of prompts for recommendation tasks (Part 2).
Explanation
Review
Task Input:
According to the feature word quality, generate a S-star explanation for user 2 about item 2.
Write short sentence to summarize the fol- lowing product review from user 2: Absolutely great product. bought this for ...
Task Output:
Absolutely great product!
Perfect!
Table 8: Comparison of independent ID (IID) with META ID on sequential recommendation task.
Sports
Beauty
Toys
Methods
@R@5 NDCG@5 HR@ 10 NDCG@10 10
HR@5 NDCG@S HR@ 10 NDCG@ 10 HR@5 NDCG@5 @R@ 10 NDCG@ 10
IID
0.0114
0.0073
0.0208
0.0103
0.0302
0.0194
0.0494
0.0256
0.0146
0.0091
0.0217
0.01 14
META ID 0.0357
0.0256
0.0520
0.0308
0.0480
0.0336
0.0689
0.0403
0.0564
0.0391
0.0803
0.0468
A.3. How to incorporate META ID with LLMs
computational demand of calculating KL. divergence for all embedding pairs in large datasets, DS employs a random sampling approach, thus present a convergence analysis for DS in Figure The stability of DS is evident across both datasets, demonstrating trend towards convergence as the number of sampled pairs grows. This sampling strategy reducesthe computational complexity from 0[1112 D) to O D), where the |7] means the size of items, D the dimension of ID representation, which is equal to the LLM's token embedding dimension.
As mentioned in subsection we convert every rec- ommendation tasks into question&answwrrigg template, in which we describe the recommendation tasks in natural lan- guage form, and replace user and item IDs with different dataset like cloze test. The full templates for every tasks in this format is from (Geng et al. 2022), where we give some examples in 6 and Table
Take rating prediction task as example. We might ask the LLM, "Which star rating will user (User) (CT) (18) give item (Item) (CTs) (24) ?", and expect the LLM to answer "5"
The approximate value of MS. The adjusted cosine simi- larity for items is given by:
Eveu : -Ru)= (Ru.j RR,)
We construct the fine-tuning and testing dataset for LLM in this unified way. Then LLM is able to acquire the general- ized knowledge across different tasks, and even carve out user and item characteristics through those tokens construct- ing IDs to handle different recommendation tasks.
sim(i. = VLNEU (Bu,, Ray2 -uEV (Ru.j R.y
(11)
where Ryy and Ru, denote user u's ratings for items i and j. respectively. while R. is user u's average rating. To enhance computational efficiency, especially for large- scale datasets, precalculate the rating deviation sums and squared sums for each item and user:
B. Details of memorization score (MS) and diversity score (DS)
Dev(i) Dev()
This section complements subsection The token embed- ding layer in LLMs transforms each input token into token embedding vectors. And we use ID representation to indi- cate that these token embedding vectors corresponding to an ID, iee,, the representation of an item or user in I.MMs.
sim' (i,)) VDevS(i) (evsS()
(12)
where the rating deviation sums and squared sums for each item is:
Dev(i) = E(Ru, - Ra); DevS(i)= = E(Rng - Rw,)
The convergence of DS. DS is metric designed to quantify the diversity of ID representations in LLMs. Given the high
uEt:
uEU;
(13)
13

---

Improving LLMs for Recommendation with Out-Or-Vocabular, Tokens
1.4
0.6
1.2
0.5
0.5
1.0
0.4
0.9
0.6
0.1
0.4
0.2
0.0
104
10
10c
10*
Number of Randoom Sample Platrs
Number Random Semple Paatra
Number of Randiom Sample
Figure 5: Convergence analysis of DS metric for Beauty and Sports datasets, with 95% confidence interval error bars indicating variability across 5 trials.
This approach reduces complexity from O[UU) 1/]²) to ((I2)), a significant improvement for large-scale datasets.
that there at least one instance included the training set for each user and item.
C. Experimental Setups and Implementation Details
C.2. Baselines
Our approach compared to a variety of established mod- els, spanning from CNN- to LLM-based frameworks. Caser (Tang and Wang 2018) applies CNNs capture high- order Markov Chains in sequential recommendation. HGN (Ma et al.
Table 9: Basic statistics of the experimental datasets.
Dataset
Sports
Beauty
Toys
2019) utilizes hierarchical gating networks for modeling long and short-term user interests. GRU4Rec (Hidasi et al. 2016) employs GRUs for session-based rec- ommendation, representing items with embedding vectors. BERT4Rec (Sun et al. 2019), $ (Zhou al. 2020) and SASRec (Kang and McAuley 2018) employ self-attention mechanisms for sequential recommendation, focusing bidirectional understanding and multi-head attention, re- spectively. FDSA (Hao et al. (2023) adopts feature-level self-attention for feature transitions. CL4SRec (Xie et al.) 2022) introduces contrastive learning with data augmenta- tion in sequential recommendation. P5 (Geng et al. 2022) learns different tasks with the same language modeling ob- jective during pretraining, serving the foundation model for various downstream recommendation tasks. P5 (Geng et al. 2022) is a recent method that uses a pretrained Large Language Model (LLM) to unify different recommenda- tion tasks in a single model. Since there is no open source code for the recent work (Rajput et al., 2023) yet, we im- plemented the key ID construction of it ourselves in subsec- tion 6.4
#Users
35,598
22,363
19,412
#Items
18.357
12,101
11,924
#Reviews
296,337
198,502
167,597
#Sparsity (%)
0.0453
0.0734
0.0724
C.1. Datasets Descriptions and Preprocessing
We conduct extensive experiments over three real-world datasets. The Amazon datasets are collected from Amazon platform with user ratings and reviews on 29 categories of products. In this paper, adopt three of them to eval- uate our method, Sports&Outdoors, Beauty, Tome& We follow (Geng et al. 2022) and use transaction records between January 1, 2019 to December 31, 2019. Detailed dataset statistics are available in Ta
We divide tasks into ratings, explanations, and reviews, adhering the data-splitting approaches of similar stud- 2020 2021). For both sequential and direct recommendation tasks, adopt the methodology of (Zhou et al., 2020: Geng et al. 2022 Ren et al. 2020), using the final item in a user's interaction se- quence for testing while carefully structuring the training data to avoid leakage. For rating, explanation, and review task families, we randomly split each dataset into training (80%), validation (10%) and testing (10%) sets, and ensure
ies (Geng et al.] 2022 Li
In particular, we provide P5 with its variations (Hua et al., 2023), equipped with different ID constructs like Sequen- tial IDs (SID), Random IDs (RID), and Collaborative IDs (CID) as benchmark for exploring the impact different ID strategies. RID Assigns each item with a random num- ber the item ID. The number further tokenized into sequence of sub-tokens, did in P5. For example, an
"hutps//mijiamo.gituuu io/amazon
14

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Improving LLMs for Recommendation with Out-Ot-Vocabulary Tokens
Table 10: Table of the size of OOV tokens used by IDs.
batch size of 64, a peak learning rate of le We apply warm-up for the first 5% of all training steps adjust the learning rate, a maximum input token length of 1024. We use the lora (Hu et al.) (022) technique to fine-tune the token embedding layer and linear head layer of aLMM72-bb for epochs using AdamW optimizer on two NVIDIA RTX A6000 GPUs with a batch size of 28, a peak learning rate of Le the lora attention dimension of 16 and the alpha parameter of 32.
OOV Tokens Size
Sports
Beauty
Toys
RID
0
0
SID
0
0
0
CID
448
437
487
IID
18357
12101
11924
META ID
1600
1319
727
item is randomly assigned the number "202""" and "2024" is represented as a sequence of tokens "20"""4".. SID is a straightforward method leverage collaborative informa- tion for item indexing, where items interacted consecutively by user are assigned consecutive numerical indices, re- flecting their so-occurrence. CID approach employs spectral clustering based on spectral matrix factorization to generate item IDs. This method is based on the premise that items with more frequent co-occurrence are more similar and should share more overlapping tokens in ID construction. The results for all baselines except P5 with its variations are reproduced through open source code (Zhou al. 2020).
For LLM inferencing. beam search is utilized to generate list of potential next items, evaluated under the all-item setting. To prevent the generation of non-existent IDs, we apply a constrained decoding method (Hua et al. 2023) setting the generation probability of invalid IDs to zero
C.3.2. EVALUATION ON RATING, EXPLANATION AND REVIEW TASKS
To validate META ID's adaptability, we extend our evalua- tion to rating prediction, explanation generation, and review tasks, akin to P5. We use the same prompts in P5 (Gieng ct al. 2022 to convert all information into training texts.
C.3. Implementation Details
For LLM fine-tuning, we pre-train T5 for 10 epochs using AdamW optimizer two NVIDIA RTX 3090 GPUs with batch size of 32, peak learning rate of 1e-3, We apply warm-up for the first 5% of all training steps to adjust the learning rate, a maximum input token length of 512 and maximum generation length of 64.
As mentioned in Appendix A we generate the META ID for users and items for each dataset, generalized to all ex- periments below. For constructing META IDs, we sampling rating-based meta-path -atths in each dataset, where adjacent users assigned equal ratings to an item. We set the sampling path length to 64, and use a skip-gram model for training, with a window size of and learning rate set at 1 The embedding clusters groups are limited to |G| = 100 (200 for Toys) to manage vocabulary size effectively. The OOV tokens size is shown in Table 10
For LLM inferencing, greedy decoding is applied for rating prediction, explanation generation, and review tasks.
The full results are shown in Table II
D. Additional Experimental Results
C.3.1. EVALUATION ON SEQUENTIAL AND DIRECT
Initialization Approaches. We explore the impact of dif- ferent token initialization methods on the performance of META ID. Recognizing that the LLM's s vocabulary includes numeric tokens for linguistic IDs, first consider whether reinitializing these numeric tokens helps LLM for recom- mendation. As shown in Figure our experiment, contrast- ing random initializing numeric tokens (Random Init.) with keeping T5's 's original token embeddings (Embedding Init.), reveals that random initialization does not enhance perfor- mance, and be detrimental on Sports and Toys datasets. This suggests that the influence of pre-training on these tokens cannot be effectively negated through simple random ini- tialization, thus not suitable for building IDs. This result emphasizes the importance of introducing extra tokens for IDs in LLM--bsed recommender systems. We also compare two initialization approaches for META ID: random initial- ization (Random Init.) and initializing OOV tokens using the augmentation (Embedding Init.) (See Section 5.2). Our
RECOMMENDATION TASKS
We first evaluate META ID on sequential recommenda- tion tasks and direct recommendation tasks following (Hua let al. 2023). Our implementation first utilizes T5 (Raffel let al. 2020b) as the backbone with parameters around 60.75 million. As mentioned in subsection 5.3 we add a linear layer where the OOV tokens undergo an extra linear trans- formation before the token embedding layer for a better initialization with a = 0.1. We also consider decoder-only architecture LLaMA2-7b (Touvron et al.) 2023) with 7B parameters. For tokenization, we use the default Senten- cePiece tokenizer with extended OOV tokens for parsing sub-word units. We use the same sequential recommenda- tion and direct recommendation prompts in P5 (Geng et al. 2022 to convert sequential information into texts.
For LLM fine-tuning, we pre-train T5 for 10 epochs using Adam W optimizer on two NVIDIA RTX 3090 GPUs with
15

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Improving LLMs for Recommendation with Out-Or-Vocabulay, Tokens
Table 11: Performance comparison of different methods on rating, explanation, and review task.
Sports
Beauty
Toys
Task Type
Metric
RID
SID
CID
META ID
RID
CID
META
RID
SID
CID
META ID
Rating
RMSF
10382
1.0486
1.0383
1.0327
1,2829
1,3098
1,2819
1.2818
1.725
1.0693
1.0766
1.0770
BLEU-1
16.2567
16.5825
16.6121
16,9005
18.2299
18.3981
19.3499
19,5106
19,9858
20.2198
20.4570
20.2270
Explan.
BLEU-4
2.1782
2.1944
2.2332
2.3481
2.9027
2.8071
3.0626
3.0592
4.3495
4.3701
4.5844
4.4945
BLEU-1
7.6140
7.7948
7.6586
7.8819
6.2282
6.5055
6,5854
7.0500
8.5336
8.0862
7.3846
8,3080
Review
BLEU-
2.3228
1.2406
2.4109
2.6546
1.8911
1.2406
1.9718
2.7485
1.1315
1.8366
1.2128
1.7061
Randun Ini. NDCGg110 10
Random: Ini. HR6610
0.07
0.07
Embolding Ini. HRA01
0.06
-8- Embelding Inz. NDCGg110
0.06
0.06
0.05
0.05
0.05
Soore
0.04
0.04
Score
0.03
0.03
0.02
0.01
0.01
0.01
SID
META
ID
(a) Sports
(b) Beauty
(c) Toys
Figure 6: Performance comparision between random initialization (Random Init.) and initializing ID token embeddings using pre-trained ones (Embedding Init.). For RID and SID, random initialization does not have positive effect compared to keeping T5's original token embeddings. For META ID, initializing using learned strategy with the help of cluster centroid embeddings (Embedding Init.) learned from meta-paths can significantly enhance the recommendation performance of META ID compared to random initialization.
shows the importance of incorporating user preferences and item attributes for LLMs enhance the accuracy of the recommendations.
HR@10 NDCG@10
0.07
0.06
Score
Visualization of ID-related tokens. Directly applying in- vocabulary tokens to construct IDs (RID and SID) brings poor performance in Toys dataset. In Figure 2b We use t-SNE visualization to map ID token embeddings and ob- served that these tokens tend to be homogeneous, whereas CID and META IDs that use OOV tokens to construct IDs have wider distribution, reflecting difference and diversity between their representations.
0.05
0.04
0.03
User-only
Item-only
User&lltem
Indexing Range
Figure 7: Performance comparison of whether indexing both items and users Beauty dataset.
To further illustrate the impact on representation, we visual- ized the attention mechanism in sequence recommendation generation in Figure X This revealed that SID leads to uni- form attention patterns, not distinguishing between different items and user IDs. In contrast, META ID demonstrates distinct attention patterns, successfully differentiating items and emphasizing user IDs, thereby allowing models to grasp more personalized and distinct information.
findings show that the latter method substantially improves META ID's performance, underlining the critical nature of the token initialization method in achieving better results.
User or Item Indexing. Previous ID strategies for LLMs only consider indexing for items, which come from the con- vention that users typically represented by sequence of interacted items in sequential recommendation (Geng et al.] 2022, Hua et al. [023 Rajput et al. (022).. While META ID models users and items, as shown in Table / reveals that the combined user-item indexing (User&Item) outper- forms either user-only or item-only indexing. This result
Statistics on training & Inference Time. We provide statistics on the training and inference time of P5 models, we collect the running time on the Toys dataset. As mentioned in subsection C we trained and test our models on two RTX 3090 GPUs. For training sequential recommendation and direct recommendation tasks, the T5 model spent 3.5 hours
16

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Improving LLMs for Recommendation with Out-Or-Vocabulay Tokens
Beauty
0,200
0.175
0.150
SID
0.125
0.075
Output Answer
</p>
0,050
user 5
item 1009
item, 1020
item 1021
item, 1022
item. 1023
item, 1024
item 1025
item 12467
0.025
Beauty
0.060
0.050
item
META ID
0.040
<CT18>
0.030
<1034>
0.020


0.010
<CT1S~
item <CT2>~10005 1053>
itm <CT18><1132>
item, <C780~<1087>
item <CTI CT18-<1134>
item <CT18>11677
item -CT18<<115>>
item, CTIS <CT18<<1277>
item, <CTI8
<1027>
Input Sequence
user
Figure 8: Visualization of attention between input and output tokens in sequence recommendation generation, where we aggregate tokens within one ID for clarity. The visualization indicates that SID leads to undifferentiated attention across items and neglects the user ID, whereas META ID distinctly allocates attention to sequence items, outputting item containing the" (CT18)" token in both the user ID and item ID with the highest attention values.
Table 12: The standard error of the metrics for META ID(T) sequential recommendation task.
Datasets
HR@5
NCDG@5
HR@ @ 10
NCDG 10
Sports
0.0322 0.00061
0,0223 0.00060
0.0487 0.00073
0.0277 = 0.00055
Beauty
0.0510 # 0.00038
0.0351 = 0.00044
0.0753 = 0.00131
0.0429 + 0.00075
Toys
0.0533 = 0.00091
0.0372 = 0.00067
0.0761 + 0.00138
0.0441 = 0.00078
Table 13: Average inference time (in milliseconds) T5 with META ID on different tasks on the Toy dataset.
Models
per user
per user-item pair
per review
Sequential
Direct
Rating
Explanation
Summarization
Preference
META ID (T)
74.05
68.60
5.21
17.28
9.67
8.55
finish training. The average inference time of TT model on dferent tasks are presented in Table 13 Sequential and direct recommendation tasks require much longer inference time than other tasks due to the beam search step. Overall, the inference is very fast. It is also promising to further reduce the training and inference time with the help of effcient Transformer techniques.
structure could be considered. Then, META ID could be applied to modern recommender system containing trillions of users and items.
E. Discussions
There are two promising directions of METА ID. First, META ID uses a fixed database of users and items, while newly appearing items and users do not have interaction history. This could solved using methods related to the cold start issue. Second, META ID applies two-level tokens for constructing IDs, while complicated hierarchical
17


Reviews:
[{"review_id": "KkvxcJCXE2", "reviewer": "Reviewer_j313", "summary": "This paper introduces META ID, a framework that improves LLM-based recommender systems using out-of-vocabulary tokens. The authors demonstrate that in-vocabulary tokens lack diversity when representing users/items and propose constructing OOV tokens from meta-path features extracted from user-item interaction histories. The approach uses clustering to create hierarchical tokens that capture relationship patterns while maintaining distinctiveness.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank all reviewers for their constructive feedback. Below, we provide a point-by-point response addressing key concerns.\n\n**Q1: Stability of DS.**\n\n**A1**: Although diversity score (DS) involves KL divergence, it is computationally efficient in practice. We **avoid full pairwise comparisons (O(N²))** by sampling a fixed number of pairs (e.g., 1,000 or 10,000), reducing the complexity to **O(S)**. Each KL divergence is fast and vectorized (via NumPy/PyTorch). For example, on the Sports dataset, computing **10,000 samples takes only 0.81 seconds** on CPU, demonstrating that DS is lightweight and scalable.\n\n\n**Q2: About the motivation**\n\n**A2:** We thank the reviewer for raising this point. We would like to clarify a potential misunderstanding regarding the term \"*ID-based*.\" Our method **does not fully rely on traditional one-hot or raw ID embeddings**, but instead **tokenizes each ID into a unique, learnable sequence**. This makes it fundamentally token-based in form while still **preserving the ID-specific semantics**—similar to TIGER 's subword compositions.\n\nOur method extends the token-based line by enabling **OOV-aware, compositional ID encoding** that bridges structured semantics with LLM-compatible inputs. Regarding vocab size concerns (e.g., DeepSeek-V3's 129K tokens), **general vocabularies lack domain-specific tokens** and might **raise hallucination** [3,4]. Our added OOV tokens (+0.7% params) cover this gap and yield measurable performance gains (Table 1).\n\n&nbsp;\n\n**Q3: On missing strong baselines such as EAGER [1]**\n\n**A3**: Thank you for highlighting recent LLM-based methods. We have added direct comparisons with EAGER on Sports and Toys datasets:\n\n**Table 1: Comparison with EAGER**\n\n| Dataset | Method  | HR@10  | NDCG@10 |\n|---|---|---|---|\n| Sports  | EAGER   | 0.0441 | 0.0236 |\n|   | Ours  | **0.0487** | **0.0277** |\n| Toys  | EAGER   | 0.0714 | **0.0505** |\n|   | Ours | **0.0761** | 0.0441 |\n\n**META ID achieves consistent gains in HR@10 on both datasets**,and comparable NDCG@10.\n\nRegarding the **HSTU[2]**, its trillion-scale architecture requires substantial resources beyond our scope. Instead of scaling size, our method emphasizes **lightweight, structured ID-level representation** (+0.7% params), benefiting OOV handling and multitask generalization. We will **include these methods in the related work section** to acknowledge their contributions and clarify our differences.\n\n&nbsp;\n\n**Q4: Comparison with traditional cold-start method**\n\n**A4**: We appreciate the reviewer’s comment and agree that cold-start has been a long-standing challenge in recommendation. While our method does leverage graph-based neighbor information via meta-paths, it differs **fundamentally from traditional cold-start solutions** in several key aspects: \n\n- **Tokenization instead of embedding fusion**: instead of fusing neighbor features or adding graph edges, we **tokenize meta-paths into ID-specific sequences** and feed them into an LLM, enabling learnable, compositional representations*.\n\n- **Unified multi-task modeling**: OOV tokens allow the model to perform **both recommendation and text generation** (e.g., explanation) in a unified token space,**which traditional methods cannot handle**.\n\nThus, our approach introduces a **novel token-level representation strategy** that integrates graph semantics into LLM-friendly inputs, a direction not explored in prior cold-start literature.\n\n&nbsp;\n\n**Q5:  Backbones and scaling law**\n\n**A5**: We thank the reviewer and agree that **scaling laws are important in GR**. We selected T5 (small-size) and LLaMA2-7B (mid-size) as representative backbones for the following reason:\n\n- **Architectural diversity**: T5 is encoder-decoder; LLaMA2 is decoder-only, covering both major LLM paradigms in GR.\n\n- **Compute trade-off**:  Both support strong performance under realistic compute budgets.\n\n- **Baseline alignment**: Baselines (e.g., TIGER, RID, SID) are also built on T5, enabling controlled comparison.\n\nWhile we do not conduct a full scale-sweep, META ID is **scaling-agnostic** and can plug into larger LLMs (e.g., GPT-3, LLaMA2-13B) without modification.\n\n&nbsp;\n\n**Q6: Number of OOV tokens**\n\n**A6**: The number of OOV tokens is **explicitly controlled** by the clustering hyperparameter (K), where each cluster maps to a token. In Figure 4, we vary K to analyze how token granularity affects performance. This mechanism offers a **flexible way to adjust vocabulary size** based on data complexity or computational budgets. For example, smaller K offers compact vocabularies, while larger K allows fine-grained modeling. [See also Q4 of Reviewer 8KDs]\n\n\n&nbsp;\n\n[1] EAGER-LLM: Efficient Adaptive Generation for Recommendation. AAAI 2024.  \n\n[2] Actions Speak Louder than Words: Trillion-Parameter Transducers for GR. ICML 2024.\n\n[3]  Survey of Hallucination in Natural Language Generation. ACM Comput. Surv. 2023\n\n[3]  How to Index Item IDs for Recommendation Foundation Models. SIGIR-AP 2023.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Diversity score as a metric is quite unstable, as each item basically requires more than 10^4 calculations, resulting in a very high computational complexity.", "theoretical_claims": "Yes.", "experimental_designs_or_analyses": "Yes.", "relation_to_broader_scientific_literature": "Close to Generative Recommendation.", "essential_references_not_discussed": "Essential References Not Discussed\n\n[1] ICML’24. Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations\n[2] AAAI’24. HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling.", "other_strengths_and_weaknesses": "Strengths:\n1.\tThe topic of generative recommendation is interesting, and the optimization from an ID-based perspective is impressive.\n2.\tThe paper is well-written and easy to understand.\n\nWeaknesses:\n1.\tThe motivation lacks sufficient justification.\n2.\tSome of the latest baselines are missing. \n3.\tThere is no discussion of scaling law, which is very important in GR.", "other_comments_or_suggestions": "1. The motivation seems unreasonable. First, id-based approaches are not essential in GR. While some GR models like HSTU and SASREC still construct input sequences based on IDs, other methods such as TIGER and EAGER-LLM have proven the feasibility and effectiveness of token-based approaches. This paper only discusses id-based methods without analyzing the advantages and disadvantages compared to token-based approaches, nor explaining in which scenarios id-based approaches are more advantageous than token-based ones. Second, existing LLM vocabularies are already very large, for example, DeepSeek-V3's vocabulary has 129,280 tokens, which is sufficient to represent clustered items. Why add separate OOV tokens? It seems somewhat redundant.\n\n2. Some important baselines are missing. For example:\n[1] ICML’24. Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations\n[2] AAAI’24. HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling.\n\n3. What's the difference between your approach and traditional cold-start item solutions? The meta-path method is similar to traditional solutions - adding neighbors through meta-paths. The novelty is insufficient.\n\n4.  Why choose T5 and LLAMA 7B? The authors need to conduct a rigorous discussion on scaling laws, which is extremely important in GR. \n\n5. How is the appropriate number of OOV tokens selected for each dataset? From Figure 4, this selection appears quite random.", "questions_for_authors": "Please see the above comments and suggestions.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NMFyHbduIw", "reviewer": "Reviewer_8KDs", "summary": "The paper proposes META ID, an Out-Of-Vocabulary (OOV) tokenization mechanism for improving user/item ID representation in LLM-based recommendation systems. While traditional methods struggle with token diversity and semantic conflicts in token representation, the proposed META ID OOV tokens, generated through clustering meta-path representations of historical user-item interactions, improve both memorization and diversity. It's tested across various recommendation tasks (sequential, direct, rating prediction, explanation generation, and review summarization), outperforming traditional ID construction methods. Experiments also show that LLM-based recommenders incorporating META ID tokens show better performance than popular non-LLM recommenders.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the **thoughtful comments and valuable suggestions**. Below, we address each concern in detail. Overall, we emphasize that our method **compares fairly with LLM-based recommenders** using equivalent architectures, maintains **high efficiency**, and introduces **minimal computational overhead**. We have also added further explanation and data to clarify the role of token size, META ID contributions, and preprocessing costs.\n\n\n**Q1：Comparisons with LLM-based recommenders**\n\n**A1**：We would like to clarify that our method **does compare against LLM-based recommenders**. Specifically, in Line 300-303, we compare with **TIGER** (based on T5X arch.), as well as **RID**, **SID**[2] and **CID**[3] (all based on T5 arch.). For fairness, META ID also use T5 architecture, with only a **0.7% parameter increase** over the vanilla T5 and achieve superior performance over these LLM-based recommenders. \n\nTo further evaluate the **feasibility and utility of META ID**, we also include experiments using mid-sized LLMs (LLaMA2-7B), which strike a balance between performance and training cost.\n\nAdditionally, tasks such as explanation generation and review understanding (in **Tables 2 and 3**) rely heavily on language comprehension, which **non-LLM models are typically unable to handle**. By leveraging the LLM, META ID effectively bridges structured and unstructured semantics—enabling multi-task capability beyond traditional non-LLM models.\n\n&nbsp;\n\n**Q2：The training and inference efficiency** \n\n**A2**: We appreciate the reviewer’s suggestion regarding efficiency. While our primary focus is on inference (as it is critical for deployment), we report **inference FLOPs for all LLM-based methods** under the same input conditions:\n\nRID：7.74 GFLOPs\n\nSID：7.74 GFLOPs\n\nCID：7.77 GFLOPs\n\nTIGER：8.02 GFLOPs\n\nMETA ID(with T5 backbone): 7.83 GFLOPs\n\nRegarding training efficiency, we note that the computational graph is structurally similar between training and inference. As such, **the relative efficiency trends are preserved during training**.\n\n&nbsp;\n\n**Q3: Performance difference w/o the Linear Transformation layer**\n\n**A3**: In Figure 6, removing the Linear Transformation layer (i.e., using random initialization) results in a performance drop. This confirms that the transformation layer contributes to more effective representation learning.\n\n&nbsp;\n\n**Q4: Token size, distinct ID space and scalability**\n\n**A4**: We agree that **token size is influenced by the number of distinct IDs**. As the ID space expands, the representational demand increases, typically requiring more clusters. However, this relationship is **not strictly linear**, as token size also depends on:\n\n- The distributional structure of the ID space (e.g., density, sparsity)\n- The clustering resolution, which controls semantic separation granularity\n\nTable 1: Token size as a function of ID count (on Beauty dataset)\n| portion | ID count | OOV Token size |\n|----|-----|---|\n| 30%   | 10,339    | 711    |\n| 50%   | 17,232    | 925   |\n| 80%      | 27,571     | 1,156   |\n| 100%     | 34,464     | 1,319   |\n\nOn testing the method under more **complex ID spaces (e.g., mixed categories)**, our current setup isolates categories to study tokenization behavior in a controlled fashion. We agree that mixed-category evaluation is a valuable direction for assessing generalizability. Although such settings are not included due to scope constraints, we **plan to incorporate them in future work**, and our method is **structurally capable** of adapting via unsupervised clustering mechanisms.\n\n&nbsp;\n\n**Q5: Computational overhead of meta-path generation and clustering**\n**A5**: We appreciate the reviewer’s concern about scalability. The proposed **meta-path generation and clustering are offline preprocessing steps**, performed once before training or inference, and thus do not affect runtime performance.\n\nOn the **Beauty dataset (~34K unique IDs)**:\n\n- Meta-path generation (using 32 walks × length 64) takes under **5 minutes**\n- Clustering completes in **8.8 seconds** using standard CPU implementations\n\nFor larger datasets, our method is scalable via:\n\n- **Parallel meta-path extraction** (user/item paths are independent)\n\n- **Mini-batch or streaming clustering** (e.g., MiniBatchKMeans)\n\nGiven the one-time nature and low cost, we believe the computational overhead is both **acceptable and controllable**, even for large-scale deployment.\n\n&nbsp;\n\n[1] Recommender Systems with Generative Retrieval. NeurIPS 2023.\n\n[2] OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems. SIGIR 2024.\n\n[3] How to Index Item IDs for Recommendation Foundation Models. SIGIR-AP 2023.", "claims_and_evidence": "1. In Table 1 the authors compared their proposed method with other sequential recommenders to demonstrate their superiority. One issue is that the other methods are not LLM-based, which makes it hard to tell where the major gain comes from, i.e. the compute and world knowledge LLM offered, or the proposed META ID. The following data points should also be presented to justify the conclusion:\n1) The training and inference efficiency related numbers of these methods \n2) Comparisons with LLM-based recommenders", "methods_and_evaluation_criteria": "-", "theoretical_claims": "-", "experimental_designs_or_analyses": "1. See the first tip in \"Claims And Evidence\"\n\n2. The effectiveness of certain parts of the model is not fully discussed, such as performance difference w/ and w/o the Linear Transformation layer.\n\n3. As a tokenization method, more discussions on ID space collisions, the relationship of token size & #of distinct IDs would help validate the proposed method. Figure 4 only presents the performances of different token size on 3 categories of the dataset, which has the following limitations: \n3.1 As shown in Table 9, the # of distinct IDs are similar across the 3 categories. How the required token size grows with # of distinct IDs is unclear\n3.2 The experiments are done on each category separately. What will the proposed method perform when ID spaces get more complex (e.g. mixed categories)?", "relation_to_broader_scientific_literature": "-", "essential_references_not_discussed": "-", "other_strengths_and_weaknesses": "-", "other_comments_or_suggestions": "-", "questions_for_authors": "Besides the questions in previous sections, \n\n1. How much computational overhead does generating meta-paths and performing clustering introduce? Will it be too computationally intensive for extremely large datasets?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NvnLu5iUsT", "reviewer": "Reviewer_Psxm", "summary": "This paper focuses on the insufficiency of the  LLM-based sequential recommendation tasks and proposes to enhance the tokenizers by introducing OOV tokens. A way of generating new tokens, namely META ID is proposed to characterize the users and items and provide token initializations for later finetuning process. Experimental results illustrate the effectiveness of META ID and introducing OOV tokens.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the constructive and encouraging feedback. We are glad that the reviewer finds our method **simple yet effective, the evaluation sound, and the overall contribution valuable**. Below, we address the concerns:\n\n**Q1: On the finetuning strategy being overly simplified**\n\n**A1**: We appreciate the reviewer’s thoughtful feedback. We would like to clarify that our method **does adopt LoRA (Low-Rank Adaptation)** for finetuning in Line 312-314, which is a widely used and effective parameter-efficient finetuning strategy, especially designed for large-scale LLMs. LoRA significantly reduces the number of trainable parameters while maintaining strong performance, and has been successfully applied to models with **tens or even hundreds of billions of parameters**, such as GPT-3, LLaMA3, and Mixtral. Therefore, our finetuning strategy is **not limited to small-scale models**, and the proposed **META ID token design is fully compatible with larger models** under the same LoRA-based training paradigm. While our current experiments focus on models up to 7B due to resource constraints, we believe the scalability of both LoRA and META ID ensures that our approach can be extended to larger models. We are actively working on testing our method on models such as LLaMA3-13B and LLaMA3-42B.\n\n**Q2: On testing with larger LLMs**\n\n**A2**: As our primary goal was to **evaluate the feasibility and utility of introducing META IDs**, we chose small- to mid-sized models to ensure manageable training costs and reproducibility. We agree that testing on larger models like LLaMA3-42B or Mixtral-8x7B is important and could better demonstrate scalability. We are currently working on extending our framework to these larger models and will include these results in future versions or follow-up work.", "claims_and_evidence": "The claims made in the submission are supported by clear and convincing evidence.", "methods_and_evaluation_criteria": "The chosen evaluation protocol and criteria are reasonable and follow the common practice.", "theoretical_claims": "NA.", "experimental_designs_or_analyses": "The experimental designs and analyses are valid and sound.", "relation_to_broader_scientific_literature": "This paper proposes a fundamental OOV token technique that could be applied in a wide range of LLM-based recommender systems.", "essential_references_not_discussed": "NA.", "other_strengths_and_weaknesses": "Strengths:\n\n+ A simple yet effective token initialization method that is agnostic to model structures.\n+ Comprehensive experiments support the effectiveness and necessity of introducing specifically-designed OOV tokens.\n\nWeaknesses:\n\n- The finetuning strategy of LLM is way too simplified, therefore might not be as effective on larger-scaled LLMs. The author could consider using other instruction tuning techniques enhance the framework.\n- The META ID method is only tested on old models with less than 7b parameters. The effectiveness of META ID on larger and latest LLMs (e.g. LLaMa3-42b) remain unexplored.", "other_comments_or_suggestions": "NA.", "questions_for_authors": "Please refer to the weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes META ID, a novel framework for improving LLM-based recommendation systems by introducing a principled approach to constructing Out-of-Vocabulary (OOV) tokens for user and item representations. The authors argue that conventional in-vocabulary tokenization schemes are semantically inadequate for capturing recommendation-specific ID semantics, and they offer a clustering-based token generation method leveraging historical user-item interactions.

The reviewers broadly agree that the paper addresses an important and emerging challenge in generative recommendation, particularly in bridging structured user-item semantics with LLM-compatible token inputs. One reviewer (Psxm) gives a clear accept, highlighting the effectiveness and general applicability of META ID across recommendation and generation tasks, supported by comprehensive experiments and a simple yet flexible tokenization mechanism. The other two reviewers (8KDs and j313) raise some concerns, especially regarding the motivation for using OOV tokens, the lack of certain recent baselines, and insufficient exploration of scaling behaviors or efficiency trade-offs.

That said, the authors provide a detailed and convincing rebuttal that directly addresses these points. They clarify misconceptions about the nature of their ID encoding (not raw one-hot), provide new comparative results with LLM-based baselines like EAGER, and demonstrate that their method adds minimal computational overhead. They also give thoughtful explanations on the token count selection, scalability, and preprocessing cost, all of which seem reasonable given the experimental scope.

While some limitations remain—notably, the evaluation is restricted to relatively small- and mid-sized models, and the paper could benefit from a broader comparison set—the proposed method is innovative, practically motivated, and supported by strong empirical results. Given the current level of novelty and the field’s interest in enhancing LLM architectures for recommendation tasks, I lean toward a Weak Accept. This work is likely to spark follow-up research and can be a valuable contribution to the ICML community.
Decision: Accept (poster)