FACE: A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens
Authors: Chao Wang
Yixin Song
Jinhui Ye
Chuan Qin
Dazhong Shen
Lingfeng Liu
Xiang Wang
Yanyong Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Recommendation System
Large Language Models
Interpretability
Primary area: applications

Abstract: Recently, large language models (LLMs) have been explored for integration with collaborative filtering (CF)-based recommendation systems, which are crucial for personalizing user experiences. However, a key challenge is that LLMs struggle to interpret the latent, non-semantic embeddings produced by CF approaches, limiting recommendation effectiveness and further applications. To address this, we propose FACE, a general interpretable framework that maps CF embeddings into pre-trained LLM tokens. Specifically, we introduce a disentangled projection module to decompose CF embeddings into concept-specific vectors, followed by a quantized autoencoder to convert continuous embeddings into LLM tokens (descriptors). Then, we design a contrastive alignment objective to ensure that the tokens align with corresponding textual signals. Hence, the model-agnostic FACE framework achieves semantic alignment without fine-tuning LLMs and enhances recommendation performance by leveraging their pre-trained capabilities. Empirical results on three real-world recommendation datasets demonstrate performance improvements in benchmark models, with interpretability studies confirming the interpretability of the descriptors. Code is available in \url{https://github.com/YixinRoll/FACE}.

FACE: A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens
Chao Wanglt+ Yixin Song't Jinhui Ye² Chuan Qin
Dazhong Shen' Lingfeng Liu Xiang Wang' Yanyong Zhang'
School of Artificial Intelligence and Data Science, University of Science and Technology of China
Hong Kong University of Science and Technology (Guangzhou)
CComuteer Network Information Center, Chinese Academy of Sciences
*Najjing University of Aeronautics and Astronautics
wangchaoai@ustor edu.cn, cn, yixinsongêmail wst.. edu. cn, jye6240connect. hkust-gz. wewzzedd.ccc cn, [chuanqin@426, d shen)egmail.com com,
Flii@mail..ust.. edu. cn, xiangwang1 223@gmail com, yanyongzeustc. edu.
Abstract
Recently, large language models (LLMs) have been explored for integration with collaborative filtering (CF)- (CF)-based recommendation systems, which crucial for personalizing user experiences. However, a key challenge is that LLMs struggle to interpret the latent, non-semantic embeddings produced by CF approaches, limiting recommendation effectiveness and further applications. To address this, we propose FACE, general interpretable framework that maps CF embeddings into pre-trained LLM tokens. Specifically, we introduce a disentangled projection module to de- compose CF embeddings into concept-specific vectors, followed by quantized autoencoder to convert continuous embeddings into LLM tokens (descriptors), Then, we design a contrastive alignment objective to ensure that the tokens align with corresponding textual signals. Hence, the model-agnostic FACE framework achieves semantic alignment without fine-tuning LLMs and enhances recommenda- tion performance by leveraging their pre-trained capabilities. Empirical results on three real-world recommendation datasets demonstrate performance improvements in benchmark models, with interpretability studies confirming the interpretability the descriptors. Code available in hhtps:: //g:::/iittt thw.. /imm/xxxnnooo/FCEE
Introduction
Recommender systems are crucial in modern digital platforms, achieving personalized adaptation and driving user engagement across e-commerce, streaming services, and intelligent education [44]
39]. Collaborative filtering (CF) [8] methods, particularly those based graph neural networks (GNNs), excel at capturing latent user-item relationships through embeddings in recommendation (38). Recently, large language models (LLMs) have shown impressive reasoning capabilities in wide prompting efforts to integrate them with recommendation systems. While some studies have explored enhancing recommendation quality with LLMs, key challenge remains: LLMs, designed for processing natural language, are not inherently equipped to interpret the latent, non-semantic embeddings produced by CF methods. Bridging this gap, which allows LLMs directly understand and utilize latent representations from CF models, remains an unresolved and pressing issue.
range of tasks [2 25
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'Equal contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Existing approaches to integrating LLMs with recommender systems can be categorized into two lypes, The first directly feeds user/item textual information (e.g,, title) into LLMs as static inputs [ 19 II. As representative example, TeLLRec [2] performs recommendations using item titles and incorporates fine-tuning for recommendation tasks. However, without collaborative information, these methods often fall short of surpassing conventional recommenders. The second approach
aligns the latent embedding space of CF with LLMs for understanding [53] I1 48). This is usually achieved through a unidirectional aligning network (e.g., MLP or Q-former (T6]). For instance,
ELM (3D equips LLMs with adapter layers for transforming abstract vectors into token embedding space; RLMRec [26] uses the contrastive or generative alignment of CF embeddings to inject textual knowledge into recommendation. While this spatial alignment helps. LLMs are pre-trained on natural textual language, and the mapped embeddings diverge from LLMs' original token space [5T]. Consequently, the implicit alignment does not enable frozen LLMs to directly interpret the intrinsic meaning of CF embeddings. This limitation hampers LLMs' capacity develop deep understanding of user preferences and to support complex downstream recommendation tasks.
To enable universal compatibility between arbitrary CF models and LLMs, several technical chal- lenges must be addressed: 1. Decoupling Entangled Representations: CF embeddings often combine multiple types of user preferences (or item features) into single entangled vector [54], making it dif- ficult to separate and interpret user's s preferences across different aspects. 2. Continuuus--o- to-sssree- Mapping: Translating continuous embeddings into -Leddaad tokens without fine-tuning requires resolving the mismatch between continuous CF representations and discrete, high nigh-dimensional pre- trained LLM token embeddings. 3. Semantic Consistency: Mapped tokens must retain the users' semantic intent while aligning with L.LMs' linguistic priors, ensuring that these representations can be effectively mapped to LLM token embeddings without distorting the information.
To address the above challenges, we propose FACE (a general Framework for mApping Collaborative filtering Embeddings into LLM tokens). Specifically, we first introduce a disentangled projection module that decomposes entangled CF embeddings into concept-specific vectors. We then design a quantized variational autoencoder 335 that learns a codebook to quantize continuous embeddings into discrete pretrained LLM textual tokens (named descriptors). Furthermore, we propose a contrastive alignment learning strategy that ensures the mapped tokens align with their corresponding textual signals. By achieving better semantic alignment, FACE not only establishes an accurate representation- semantic mapping between general CF models and LLMss without fine-tuning L.MSs but also leverages the powerful capabilities of LLMS to enhance the performance of the original recommendation model and interpret its embedding. Finally, extensive experiments on three open-access datasets demonstrate that FACE improves the performance of conventional CF models, while interpretability studies validate that the decoded tokens align with textual signals and provide embedded interpretations for user-item interactions 12 Our main contributions can be summarized as follows:
To the best of our knowledge, this is the first work to propose a model-agnostic framework for directly mapping CF user/item embeddings into pre-trained LLM textual tokens, allowing LLMS to better understand user preferences and support more complex recommendation tasks.
We introduce FACE, a general and interpretable representation alignment framework that leverages vector quantization and contrastive learning efficiently and accurately convert entangled CF embeddings into pre-trained textual tokens.
Extensive empirical results on three real-world datasets show that FACE enhances recommendation performance across various benchmark approaches, while interpretability studies demonstrate its great interpretability.
2 Related Work
Collaborative Filtering. Collaborative filtering (CF) a technique used in recommendation sys- tems that makes predictions based on preferences or similarities. Matrix factorization (MF) [4 37 the prototype of collaborative filtering based on representation learning, where the embedding matrix of entities (users & items) is learned directly. Inspired by the success of graph neural net- works (GNNs) in modeling graph-structured data 13,, many studies have explored grapd representation learning by modeling the user-item bipartite graph structure. Simplified graph collab- orative filtering models that eschew complex deep learning operations, such as I.RGCCF [ and LightGCN 9 have demonstrated superior performance in experiments. Subsequently, numerous
2

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studies have focused on optimizing graph collaborative filtering models. For instance, some have utilized self celf-superviee learning methods to enhance the robustness of representations 43 50 20].
while others have addressed issues like over-smoothing [ and over-correlation [46.. Despite these encouraging advances, pure collaborative filtering methods consistently struggle with data sparsity and explainability in practical applications.
LLM-based Recommendations. Advances in LLMs have sparked significant research interests in enhancing recommender systems. Much prior work is devoted to utilizing or training LLMs to function as effective recommenders. A straightforward approach is to list the historical interaction item sequence and design prompts to guide LLMSs to make recommendations 6 7. and TAL..ec further incorporates tuning into LLMs for the recommendation task adaptation to bridge the gap between recommendation tasks and natural language tasks. Recently. research has shown that LLMs can comprehend recommendation embeddings via lightweight adapters [49 31, and can be aligned with -ppeiic latent embedding spaces [32]. Building on this, some works integrate collaborative information to improve LLM's performance in recommendations. For instance, CoLLM /53M/ [T]] combines mapped collaborative information with item titles into LLM recommendations for better performance/explanaan
ILM [88] adopts the Q-former architecture to encode item CF embeddings for conversational recommendation. However, the mapped embeddings diverge from the LLM's original token space, which hinders the full utilization of the LLM's capabilities. To address this challenge, BinLLM [52 encodes collaborative information textually into IP-address-like string. However, relying solely numerical tokens limits the LLM's comprehension and still necessitates fine-tuning for L.MMs to understand the collaborative information. Moreover, owing the limited context window and high computational costs of LLMs, these LLM- based methods can hardly be applied to a full-ranking setting in real-world recommendation scenarios, thus suffering from scalability issues.
Another stream of work concentrates on enhancing existing collaborative filtering methods with the assistance of LLMs. Extracting and injecting the world knowledge and context comprehension
abilities of LLMs into conventional recommender systems could enhance item and user modeling (3)
LLMRec 42 augments semantic and collaborative data with the power of LLMs, and RLMRee (6
aligns the final representation of users and items obtained from the backbone of a conventional recommender (e.g., LightGCN) with the profile generated from text embedding model. However, the LLM's capabilities are not being fully utilized due to the large gap between text and recommendation. Differently, we encode the CF embeddings into the token space of the LLMs. By doing so, pre-trained LLM can directly understand and interpret CF embeddings through its inherent literal knowledge, without requiring additional fine-tuning.
3 Methodology
3.1 Framework Overview
The FACE framework transforms CF embeddings into textual tokens (descriptors) compatible with LLMs. As illustrated in Figure D the framework consists of two main steps. In the first step, vector-quantized disentangled representation mapping (Section 3.2). employ an AutoEncoder architecture with a quantized codebook of token embeddings following dimensionality reduction. The encoder, composed of projei-projector and transformer, disentangles the original CF embeddings and learns complex relationships. In the second step, contrastive learning for semantic representation aligmment (Section 3.3), we utilize an LLM embedding model encode user/item summaries and descriptor-generatee sentences, which are then aligned through a contrastive learning objective.
3.2 Vector-quantized Disentangled Representation Mapping
To enable effective mapping of CF embeddings with pre-trained LLM tokens, inspired by VQ- VAE 35), we propose to apply frozen L.LM vocabulary as the codebook, with the encoder-decoder structure to learn the mapping relationships.
3

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(n) Mupping
item
Lincar Trazsform W,
Encoder
Finbs
Decoder
vocabulary
Codebook Co
CF Model
Disentangle
Recomstuct
aBQ BB
Nearest
aee
Rasidual
GNN
(24)
Sum
Muhti projector
Transformer
Residual Quantization
Transformer
Concat projector
(b) Alignment
The described as none
Description
Prempt
Deertptor
LLM Embedding
LLM Inchance
Wanderbee
Summaries
Latign
b
Contrastive Alignment
Figure The overall architecture of our FACE framework. (a) Mapping stage. We employ framework similar to the RQ-VAE architecture for the final embedding of CF model with frozen LLM codebook, encoding the CF embedding into pre-trained LLM tokens. (b) Alignment stage. We leverage contrastive learning achieve the semantic alignment of descriptors and summaries.
3.2.1 Codebook for Quantization
This part focuses on constructing a codcbook with LLM's vocabulary for quantization. First, we conduct filtering the vocabulary of the LLM. Specifically, with the assistance of the Corpus of Contemporary American English (COCA) [4], we select words (no subwords) that carry meaningful semantic information for interpretation. Then, obtain subset D DLLM of the LLM vocabulary, where DLMM represents the full vocabulary of the language model. We freeze the embedding of these tokens as the codebook in quantized autoencoder structure, formulated as:
Co (D)
(I)
where ELLM denotes the pretrained token embedding component of the LLM, and the embedding matrix Co E IlP) xdux, These token embeddings encode rich semantic information, enabling further mathematical operations the vector space to quantify semantic associations. To reduce redundancy in high-dimensional LLM token embeddings after filtering, we apply trainable linear transformation:
C W.Co
(2)
where W. E RRdxdux, and (d < dLLM) is the dimension for quantization. This dimensionality reduction not only enhances computational efficiency but also initiatively aligns LLM tokens with CF embeddings. Crucially, we freeze the original codebook embeddings Co while exclusively updating the projection basis vectors through W This design aligns with the SimVQ approach [55], where theoretical analysis demonstrates that optimizing the latent space geometry rather than individually selected code vectors mitigates representation collapse 29
3.2.2 Representation Disentanglement and Quantization
To ensure the integrity information as far as possible while mapping the CF representation to the vocabulary space, adopt the architecture of the autoencoder, which is designed learn latent space that is capable of recovering the original data. The encoder is a disentangled projection module consisting of two main parts.
First, the final representation e the backbone CF model passes through multi multi-projectov which projects this vector into different directions, capturing information from various perspectives:
e; W,e for = 1,
(3)

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where W denotes the weight matrix of the i-th projection head. The projection heads are initialized orthogonally. These weight matrices project the origin embedding into n new spaces, aiming to disentangle the complex preferences implied in the CF embedding into multiple aspects.
Secondly, the obtained n vectors encoded using a transformer encoder module [3 capture the relationships between them and perform non-linear mapping:
(Zeg Rogs Re..) = (ransformer..e1, e2, Cn.)
(4)
Residual Quantization. For each Ze, it is then quantized with the codebook C (c1, C22 02,... .p) constructed in Section 3.2.T by recurrently selecting the nearest codeword to the current residual measuring by Euclidean distance. For layer h, the residual quantization (RQ) [15) is expressed as:
(h) p(++1) = = C w.I.t k= arg min ijm [|r(() c211..
a
(5)
where rh is the residual vector in the /-th RQ level, and initially r()) = Ze. After H levels of residual quantization, the quantization embedding of z. can be obtained by =. = Li= c(h), To facilitate backpropagation through this non-differentiable operation, the Straight-Through Estimator (STE) is applied between 2e and 2g. Compared with vanilla quantized autoencoder (e.g.. VQ-VAE), RQ quantizes residuals through multiple stages to achieve more precise discrete representations and higher reconstruction quality. The hierarchical structure of residual quantization allows the model to capture different levels of semantic information. Unlike conventional residual quantization approaches, our implementation shares the same codebook across all quantization levels, with this codebook being composed of pre-trained LLM token embeddings, maintaining semantic consistency among the quantized tokens at different hierarchical levels. When obtaining 2g by progressively summing the quantized vectors from each layer, it can be implicitly interpreted as a composition of meaningful semantic tokens. The descriptors can be obtained from the first-level quantization c(1) as the nearest vector captures the main information of Ze. For simplicity, we denote the descriptor embedding as 2,, = c( in aspect i.
H
Similarly, the decoder consists of transformer followed by concat-projector to fuse the disentangled quantized embedding to get the recovered CF embedding ere- The transformer in the decoder shares the reverse architecture of that in the encoder. And it seems like simplistic embedding model, which generates the embedding for recommendations from several words. Furthermore, the decoder can be used as generator: given a few keywords, the well-trained decoder can generate primary embedding for users/items. The decoder can be expressed as:
(p1, Po..P........ .... Pn.) Transformer (291: Eq2 ... (qn.)
(6)
Cre W Concat(Pl, ....... ... .P)) + b.
The loss function for mapping the embedding into descriptors appears similar the RQ-VAE loss. The reconstruction loss ensures that the framework can effectively reconstruct the input embedding e, while the quantization loss constrains the encoder and the codebook, ensuring the encoder output z, close to the codebook vector 2g and vice versa. It can be formulated as (sg[-] denotes stop gradient):
Lrccons = logg(elzz) = -leere - -e112-
(8)
(1) CQ= = (M)) -c Bllsglo (1) 1 -
(9)
h=1
Emap Crecons + Lq.
(10)
3.3 Contrastive Learning for Semantic Representation Alignment
To achieve semantic alignment between CF representations and M.de-vvee embeddings and ensure the interpretability of mapped descriptors, we propose a contrastive representation alignment strategy, the goal of which to align the synthetic semantics of descriptors with the corresponding textual information of users/items, ensuring the coherence of the mapped descriptors with the text.
5

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3.3.1 Semantic Embedding with LLM
Due the excellent comprehensive capability of LLM, high-quality text embeddings can be obtained from LLM, especially those adapted for embedding tasks [22). through token pooling. We will show how to process text from summaries and descriptors and generate semantic embeddings from LLMs.
Summary Embedding: The summary embeddings are derived from textual information in the dataset, and they serve as the anchor for alignment. Following the paradigm established in RLMRec (26), we first generate a structured textual summary, denoted as s, for each user and item. This is achieved by employing LLM to enhance the original raw text. The process can be formulated as:
$ = LL, + Tori),
(11)
where P. is predefined prompt and @ denotes the concatenation operation. The original text, Toris is compiled from different sources: for items, it consists of the title, description, attributes, and any available user reviews; for users, it is composed of the summaries of their historically interacted items. Once the enhanced summary s is generated, it is encoded into a dense vector representation h, using an LLM-based embedding model E:
h, = {s).
(12)
Descriptors Embedding: The embedding process of descriptors is designed to generate comprehen- sive semantic representations for users and items from their extracted descriptors. The first step is to format the descriptors into sentence-like input. We prepend prompt Pa to the descriptors, which specifies the entity type (user or item) and indicates that the subsequent descriptors are keywords. could be: "The reader and his
For example, in a book recommendation context, the prompt for preference can be described as:". However, directly generating embeddings from the textual form of the descriptors is eiffeeifeeeet operation, which prevents gradient flow during training. To ensure end-to-end differentiability. we map the descriptors (Zd, Edg...... =d.,) back to the original high-dimensional word embedding space. This is accomplished by applying inverse transforma- tion via the pseudo-inverse matrix W-- = (W"W - -WT These recovered embeddings are then concatenated with the prompt's 's token embeddings to form the final input sequence dl as a description of the user/item:
d (LLM(Pa)) + (W.
Zd, 1 Edz. W-1
(13)
Finally, the complete sequence of embeddings d is passed directly the LLM encoder & to produce the final descriptors embedding hy:
h Edd).
(14)
3.3.2 Contrastive Alignment Learning Strategy
To train the words and align the semantics, we adopt the contrastive alignment learning loss 223 Latign to learn the distinctive features users and items, which can represented as follows:
o(dr,
Calsgn
log
(15)
Even ((v,
VEQ
where Q is the current batch of data containing users and items, and ((d, s) is function that computes the matching score between descriptor geeeerreeee sentence d and summary s. In this paper, we adopt the cosine similarity with temperature T:
((d, = =(xps) cos cos(h,, h2)).
(16)
This contrastive alignment learning strategy minimizes the distance between descriptors d, and their corresponding fixed summary 8u, while pushing d, away from non-corresponding summaries sur. By contrasting positive (d.,, 8,) pairs against negatives (do,&r)) &w)). the descriptors adapt to emphasize unique attributes of their paired user/item, rather than generic features. h, can be generated in advance and fixed during training, and it serves as stable semantic anchors, ensuring learned descriptors capture discriminative features for alignment.

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Table 1: Overall performance comparison on Amazon-book, Yelp, and Steam datasets.
Amuzon-buol
Yelp
Steam
R@5
R@200
N@5
N@20
R@5
R@20
N@5
N@20
R@5
R@200
N@5
N@20
GMF
0.0615
0,1531
0.0616
0.0922
0.0372
0.1052
0,0433
0.0660
0.0523
0.1343
0.0567
0.0844
FACE
0.0658
0.1553
0.0639
0.0955
0.0414
0.1120
0.0483
0.0717
0.0547
0.1411
0.0594
0.0888
LighiGCN
0.0659
0.1563
0.0657
0.0961
0.0421
0.1141
0.0488
0.0726
0.0530
0.1361
0.0584
0.0862
FACE
0.0705
0.1622
0,0705
0. 0.1009
0.0446
0.1203
0.0819
0.0766
0.0559
0. 0.1439
0.0611
0.0912
SinOCL
0.0695
0.1617
0.0693
0.1001
0.0447
0.1209
0.0529
0.0775
0.0550
0.1420
0.0605
0.0899
FACE
0.0742
0.1670
0.0737
0.1047
0.0461
0.1225
0.0534
0.0781
0.0594
0.1487
0.0649
0.0951
LightGCL
0.0810
0.1712
0.0811
0.1114
0.0452
0.1228
0.0530
0.0780
0.0526
0.1234
0.0576
0.0815
FACE
0.0832
0.1759
0.0842
0.1148
0.0455
0.1253
0.0533
0.0793
0.0528
0.1238
0.0585
0,0818
RLMRec
0.0669
0.1572
0,0063
0.0081
0.0426
0.1165
0.0495
0.0737
0.0545
0. 1400
0,0599
0.0887
FACE
0.1581
0.0985
0.1196
0.0755
0.1432
0.0604
0.0901
3.4
Optimization
The above framework is employed on the final representation of the basic collaborative filtering model R. where prediction is performed on this representation. In other words, our proposed approach model-agnostic. Any model that can perform representation learning for users/items can be aligned with LLMs through the framework. Assuming the optimization function of the recommender R. is denoted as LR, the overall optimization objective L can be formulated with coefficient j and A
L= + pLmmp + Xaalign-
(17)
Nevertheless, directly optimizing the joint objective would lead to unstable training dynamics. This is because simultaneously learning the recommendation backbone parameters from scratch and the discrete L.L mapping function creates conflict. For the sake of training stability, we adopt a 3-step training strategy. In step 1, pre-train the backbone recommender independently of our framework: in step 2, employ the quantized autoencoder structure without alignment to primarily map CF embeddings into LLM token embedding space; in step 3, add the semantic alignment to the framework and jointly optimize with the whole objective min L. This curriculum paradigm ultimately enables the alignment from the recommender to the LLM while enhancing its original capability and interpretability.
4 Experiments
In this section, we conduct extensive experiments to evaluate the performance of FACE in comparison to various state-of- state-of-the-art the-art models across three real-world datasets. Specifically, we validate the performance of the recommendation, the interpretability of mapped descriptors, the ablation studies, and the sensitivity of the model.
4.1 Baselines
We evaluate the effectiveness of our framework by integrating it with five widely used state-of-the recommendation models: GMF [4] decomposes the interaction matrix into latent representation; LightGCN [9: : simplified graph convolutional network removes conventional neural components;
SimGCL 150: : a contrastive learning framework without explicit data augmentation, generating multiple views via perturbations of the embedding; LightGCL 288: leverages SVD to generate self-augmented representations; RLMRec [26): aligns existing CF models with LLM by maximizing mutual information. Its contrastive variant based on LightGCN is used as the base model.
4.2 Experimental Settings
The evaluation protocols for recommendations employ two widely adopted ranking metrics: Recall@N and normalized discounted cumulative gain (NDCG@) @ N) [10] with N=(5, 20), calculated through an all-ranking evaluation strategy [40]. These metrics consider all non-interacted items potential candidates for each user during recommendation.
Evaluations:
Datasets: We conduct experiments of the base model and our FACE framework on three public datasets: Amazon-book book, Yclp, and Steam. Please refer to Appendix for for dataset details.
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131
Velp
Stram
Amazon
Steom
Acoureyy
0.8
0.8
0.8
Similariy
0.7
0.7
0.7
0.6
0.6
0.2
10
20
30
40
50
60
10
20
30
50
60
10
20
30
40
50
60
Namber
Nunber Candidaaes
Nunba Candidans
All
Trush
All
Trumh
All
Trath
Figure 2: Item-retrieval task.
Figure 3: Item-generation task.
Implementation Details: Our proposed framework is a model-agnostic plugin. Therefore, the recommendation objective is that of the base model. In experiments, the collaborative filtering base models all adopted the BPR recommendation loss function as their objective. To ensure fair comparisons, the latent embeddings for all baselines and FACE are set to 256. We determine the main hyperparameters through a grid search. For FACE, we use the pre-trained embedding model adapted from LLaMA2-7B (34] the LLM. Additionally, all three datasets are divided into training, validation, and test sets with a split of 3:1:1. FACE is trained using the Adam optimizer, with learning le-2 and batch size 128. We implement the model with SSLRec (27 and deploy NVIDIA A100.
4.3 Performance Comparisons
To validate FACE's effectiveness enhancing the performance recommender backbones, we conduct comparative analysis of several base models integrated with FACE against state-of-the art methods in collaborative filtering and the L.LM-enhanced recommendation model. We run the models three times and report the average results, which leads the following conclusions: (1) FACE enhances the performance of various backhones, with maximum gains of 7.31 %, 11 11.55% and 8.00% on the Amazon-book, Yelp, and Steam datasets, and nearly sorts of CF models can benefit from FACE's integration. The empirical results highlight FACE framework can be used as general descriptor generator without the loss of performance and could even serve enhance the existing CF model from the text alignment. (2) Moreover, our framework can be applied to RLMRec, a collaborative filtering model that already incorporates textual information, achieve further performance improvement, even if the improvement is minor compared to its application on traditional recommendation models. These comparative results demonstrate that our proposed framework, especially its descriptor-bant alignment strategy with large language models, exhibits superior effectiveness enhancing CF performance.
4.4 Interpretability Studies
4.4.1 Item Recovery Based Descriptors
Item-retrieval Task: To demonstrate that the implicit semantic information descriptors can be understood by LLMs. We first introduce an item-retrieval task, aiming to leverage the linguistic capacity LLM retrieve the original item based solely on the FACE-generated descriptors. The candidates are / items, each of which is represented by its basic information (e.g. title and summary). We ask the LLM to select the most relevant item from the candidates based on the descriptors, and the retrieval process is evaluated by the accuracy, shown in Figure The result primarily indicates that the descriptors can effectively capture the semantic information of the items, as the LLM is able to retrieve the original item with high probability. However, the accuracy of the item-retrieval task affected by the number of candidates I, and the more candidates there are, the more challenging becomes for the LLM to retrieve the original item.
Going a step further, we propose an additional item-generation task which the LLM is asked to generate both an item and its description based solely on the given descriptors. To assess the quality of the generated items, we follow the methodology outlined in Section 3.3.1 to obtain their summary embeddings. Then, caleulate the cosine similarity between the summary
Item- semm-generation Task:

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embeddings of the generated items and those of the original items in the dataset. Ideally, the generated items should be similar the truth item (the source of these descriptors). We report the distribution of cosine similarity between generated items and all the original items (AlI), and compare it with that between generated items and their corresponding truth items (Truth) in Figure [ The whiskers represent the non-outlier range, with outliers defined as values beyond 1.5xIQR from Q1 or Q3.
The results show that the generated item significantly more similar the truth item than others in the dataset, indicating that the descriptors can effectively capture the semantic information of the items. This suggests that LLMs can understand and generate items based on the descriptors, demonstrating the quality of FACE-genee -egnerrre descriptors and their potential for enhancing the interpretability of recommendation systems.
4.4.2 Real User Study on Interaction Interpretation
We conduct a real-user evaluation to assess how convincingly descriptor-based interpretations reflect the implicit relevance of user-item interactions, focusing specifically the interpretability of descriptors in explaining user-item interactions, and the key lies in whether the LLM can understand the implicit relevance of these descriptors. We ask LLM generate interpretations for user-item interactions based on the descriptors, and then we ask human annotators and another L.M to rank the interpretations based their reliability. This experiment is designed to evaluate the quality of the generated explanations and to assess how well LLM can understand and explain.
To be specific, conduct the interaction interpretation experiment, respectively based on RLMRec profiles (LLM-generated profiles from textual information) and FACE descriptors. We randomly sample 40 users from the Amazon dataset along with their interacted items. For each user, one interacted item from the test set is selected as a positive sample, while three non-interacted items are chosen negative samples. Next, provide LLM with the user and item profiles/descrriporrs and instruct it to assume an interaction between them, prompting it to generate an explanation grounded in the implicit relevance of the given information. Finally, ask ten human annotators and another LLM (DeepSeek v3) to rank the four candidate explanations for each user based on their reliability, where higher-ranked explanations are expected to correspond the positive samples.
The results in Table 2 indicate that the relevance between sets of descriptors be utilized by LLMs to interpret interactions. Furthermore, the ranking results show that the explanations gen- erated based on FACE descriptors those based on RLMRec profiles, and it is worth noting that a set of descriptors contains only 16 tokens compared to paragraph of profile, suggesting that descriptors can be more efficient in capturing the implicit relevance of semantic information in user- item interactions and providing explanations. These interpretability studies quantify the quality of semantic mapping and suggest that our method improves the interpretability of CF embedding.
Table 2: Ranking Results.
slightly more reliable than
Method
Manual
LLM
RLMRsc Profile
1.935
L8000
FACE Descriptors
19915
1.700
4.5 Hyperparameter Analysis
In this part, we carry out a hyperparameter analysis on the Amazon dataset with GMF+FACE and LightGCN+FACE, concentrating three crucial hyperparameters: descriptor number n. codebook dimension d, and the alignment weight X. The results can be observed in Figure
Codebook Dimension means the degree of semantic preservation for tokens before and after dimen- sionality reduction. Lower dimensions (64D) limit the semantic capacity of tokens, whereas higher dimensions (512D) may cause overfitting.
Descriptor Number plays significant role in reconstruction and alignment, and meanwhile, it means the number of components after disentanglement. Increasing descriptors from to 16 demonstrates an overall upward trend in performance, indicating that more components and descriptors preserve the preference contained in CF embeddings more comprehensively. However, 8 to 16 descriptors already possess sufficient information. When it is larger, descriptors suffer from duplication.
Alignment Weight controls the injection of textual signals and significantly influences the perfor- mance. A larger À injects more text information into the CF model. However, when it is too large,

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Codebook Dimension
Descriptor Number
Alignment Weight
Recalig/20
0.160
0.160
0.160
0.155
0.155
0.158
LightGCN
GMF
Figure Analysis results on hyperparameter sensitivities.
the recommendation task will be hindered. Conversely, when it is relatively low, the performance drops with a poor alignment result.
4.6 Ablation Studies
We conduct ablation studies on Amazon-bool and Yelp datasets with LightGCN as the base model to validate the necessity of key components in our framework. Four variants compared: (I) Full: the complete FACE model; (2) w/o trans: FACE with the transformer module removed from the autoencoder; (3) w/o recons: FACE excluding the decoder for reconstructing the original input embeddings; (4) w/o align: FACE without the semantic representation alignment step. The results in Table show that the full model achieves the best performance on both datasets.
Although the transformer module has rela- tively minor overall impact, its complex trans- formation process can enhance the quality of the descriptors, as the self-attention mechanism enables effective communication among disen- tangled embeddings. Additionally, the absence of reconstruction negatively impacts the perfor- mance, and it is noted that in this variant, de- scriptors suffer from the lack of diversity, result- ing in the loss of information in the one mapping process. Notably, without alignment, the metric drops dramatically, demonstrating that the step of contrastive learning for semantic representation alignment plays significant role in enhancing performance by equipping CF models with semantic information.
Table 3: Ablation studies.
Variant
Recall@
NDCG@20
Fall
0.1622
0,1009
Amazon-bank
who
0,1611
0,0994
wfo recons
0. 1586
0.0981
wlo align
0.1565
Full
0.1203
0.U766
Yelp
who
0,1200
0.0762
wfo recons
0.1191
0.0760
wlo align
0.1171
0.0741
Conclusion
In this paper, we introduced FACE, a novel framework that bridges the gap between collaborative filtering models and large language models by mapping CF embeddings into the semantic tokens of LLMs. Through a disentangled projection module and a vector-quantized variational autoencoder, FACE efficiently converts continuous CF embeddings into discrete, LLM- LLM-compatible tokens. A contrastive aligmment objective ensures that these tokens maintain semantic consistency, enhancing the interpretability and performance of recommendation systems. Our extensive experiments three real-world datasets demonstrate that FACE can improve the performance of various CF models. Additionally, interpretability studies confirm the improved interpretability of the deseriptors.
Acknowledgments and Disclosure of Funding
This work was supported part by the National Natural Science Foundation of China (Grant No. 62506348), the Natural Science Foundation of Anhui Province (Grant No. 2508085QF211), the CCF-1688 Yuanbao Cooperation Fund (Grant No. CCF-Alibabaa025005), the National Natural Science Foundation of China (No. 62406141), the China Postdoctoral Science Foundation (No. G7CC0255740), the National Natural Science Foundation of China (Grant No. 62332016), and the iFLYTEK Cooperation Project.
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Answer: [NA]
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A More Implementation Details
A.1 Dataset Details
We conduct experiments on three benchmark datasets: Amazon-book. Yelp and Steam: Amazon- book derived from the Amazon Product Review corpus, focuses on literary products, containing user-book interactions, review texts, and product descriptions; Yelp which consists of user-business interactions from metropolitan services, featuring categorical attributes and consumer feedback; and Steam collected from the digital game distribution platform, containing user-game engagements with gameplay reviews. Dataset statistics are shown in Table 4
Table 4: Statistics of the datasets.
Dataset
Users
Items #
Interactions
Density
Amazon-book
11,000
9,332
120,464
1.2e
Yelp Steam
11,091
11,010
166,620
1.4e-5 3
23,310
5,237
316,190
2.6c
A.2 Hyperparameter Settings
For our FACE framework, the number of descriptors is set to 16, and the dimension of the codebook to 256. The temperature of alignment loss is fixed 0.02, and the coefficients 3. l and X set to 0.25, I and 0.1, respectively. For the parameters of baselines or base models, we mainly use the official setting from the SSLRec benchmark platform for fair comparisons, except for those we have stressed in the paper.
B Performance with Other LLMs
We implement our FACE framework using several more representative LLMs, including all-MiniLM- L6-v2 [4] and gte- -wen---w--nn--tteeeeeeeee architectures and scales. Table 5 reports the performance of FACE when integrated with these LLMSs on the Amazon-book Yelp, and Steam datasets. The metric N denotes the accuracy of the item-retrieval task (Section 4.1 with N candidates.
[18] to evaluate its generality across different language model
Table 5: Performance FACE with different LLMs on the Amazon-book dataset.
Model
R@5
@@200
N@5
N@20
I@ 10
I@50
LightGCN
0.0659
0.1563
0.0657
0.0961
+ FACE (LLaMA2)
0.0705
0.1622
0.0705
0.1009
0.9116
0.7024
FACE (MiniLM)
0.0703
0.1631
0.0698
0.1012
0.9105
0.7394
+ FACE (Qwen2)
0.0692
0.1623
0.0682
0.1006
0.8703
0.6291
Table 6: Performance of FACE with different L.I.Ms on the Yelp dataset.
Model
R@5
R@20
N@5
N@20
I@ 10
I@50
LightGCN
0.0421
0.1141
0.0488
0.0726
FACE (LLaMA2)
0.0446
0.1203
0,0519
0.0766
0.8712
0.6469
+ FACE (MiniLM)
0.0445
0.1191
0.0518
0.0762
0.8907
0.6322
FACE (Qwen2)
0.0449
0.1193
0.0517
0.0761
0.8356
0.6158
hhtt://jmccaleyy.ssd.cciii hwtp:///......uuuuddd//aaamm////
hhtp:///wwwwkkgggg..ommmmtttttee games/data
22

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Table 7: Performance of FACE with different LLMs on the Steam dataset.
Model
R@5
R@20
N@5
N@20
I@ 10
I@50
LightGCN
0.0530
0.1361
0.0584
0.0862
FACE (LLaMA2)
0.0559
0.1439
0.0611
0.0912
0,9330
0.7322
FACE (MiniLM)
0.0557
0. 0.1425
0.0608
0.0899
0.9287
0.7079
+ FACE (Qwen2)
0.0558
0.1436
0.0611
0.0908
0.8753
0.6194
The results demonstrate that FACE consistently enhances interpretabilityy while maintaining com- petitive recommendation accuracy across different underlying LLMs, underscoring the robustness and flexibility of our framework. Notably, the three LLMs achieve comparable performance when integrated with FACE, indicating that the choice of LLM (e-g., smaller models like MiniLM or larger ones like Qwen2) does not substantially affect the framework's s effectiveness. However, Qwen2 exhibits marginally inferior performance, likely attributable its specialization in query-document matching rather than semantic similarity. These findings reinforce that FACE is model-agnostic and can be seamlessly adapted to diverse LLMs, rendering it suitable for a broad spectrum of practical recommendation scenarios.
Furthermore, this approach reveals the potential to employ small-scale language models for vocabulary and embedding generation, thereby significantly improving efficiency and reducing computational resource demands when mapping CF embeddings semantic tokens. Subsequently, large language models with superior generative capabilities can be leveraged for downstream tasks-such as explana- tion generation, large-model-bate recommendations, and controllable recomnendationn--bildinn upon the descriptors produced by the smaller- FACE.
C Performance Comparing with Other LLM4Rec Methods
To fairly compare the ability of other frameworks to align CF with LLMs for textual information injection and performance improvement, LightGCN, and investigate the effect of further applying FACE to these LeMeennaeced recommenda- tion models. The experimental results in Table demonstrate that FACE can be effectively applied to various existing LLM-enhanced recommendation approaches to further improve their performance. These findings are consistent with those presented in the original paper, highlighting the advantages of the FACE framework in utilizing semantic mapping to enable LLMs to comprehend collaborative embeddings.
adapt CTRL (17) and KAR 47 the base model
Table Performance comparison of FACE-enhanced models on three datasets. The best results highlighted in bold.
Dataset
Metric
CTRL
CTRL+FACE
KAR
KAR+FACE
R 5@5
0.0685
0.0699
0.0671
0.0712
0.1566
0.1612
0.1547
0.1559
Amazon
N@5 5
0.0666
0.0706
0.0660
0.0714
N
0.0963
0.1013
0.0952
0.1002
R R@S
0.0413
0.0432
0.0408
0.0446
@ 20
0.1161
0.1189
0.1132
0.1249
Yelp
N@s 5
0.0471
0,0510
0.0476
0,0518
N@20 20
0.0719
0.0755
0.0712
0.0781
R@5
0.0531
0.0569
0.0502
0.0516
R @@20
0.1375
0.1461
0.1322
0.1349
Steam
5@5
0.0586
0.0620
0.0553
0.0571
N N@20 20
0.0865
0.0922
0,0826
0,0852
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To verify the quality of the descriptors mapped by the model, Section 4.4.1 proposes an item- Case Studies
D
D.1 Example for Item-Generation Task
generation task in which the LLM is asked to generate both an item and its description based solely on the given descriptors (prompt template in Appendix [E). And then we generate its summary to compare with the original one. To facilitate deeper understanding of the process, we present a case study illustrated in Figure [ It can be discovered that the summary of the generated item closely resembles the original summary (the summary of the corresponding item in the dataset), and is not similar to the non-corresponding item. This indicates that the descriptors generated by FACE framework are effective in capturing the semantic information.
Descriptors
angel, romanoe, faast, adminig pain, anguish, drow, hope, novel, baggard, growth, emotion, beaving brooding, salemn, pelicth
Summary of Non Con-Correspnnnnngg Item
histocical fiction those interssed Civil War enjoy Neadful R ver igainst bockdmp of milltary conflicts and social atrife, this novel expiors the lives of both Confedeerte lovalist and Union abolitomets, offering compelling view of the neuiting changis and subsaquent impicts on nearlyy communitios.
G Comer aled Tum
"Glle" "A ngells in the Stomn: Romance Forged in nguish"" description': 'Anid devastatnng floods specakypac chaos, soult find uneepes ted hmoding surnivor must navigate you hope ther becomes the only light crowzed wodld")
empelie hedler
similarity: 0.24
Summars of Geseraled ltem
Corresponding Ttem
Summary of
Readers who drowa emotoonal character stanes backdmop cotrstrophe coptivattd the Storm: Romance Forged nguich.
Reaiers who enjoy contemporary romance novels with strung emotional connections and touch humor would enjoy moding Gabrel's a ngeel.
post sporalypric nuteacce- especially those who appriciale stories bealing, indenptionn and love blooming amid divedaation will find this zovel particuladly moving. These who seek a lender yet intense love staped insutie survivel, boge will not want miss bountingly
feferrees damaped hern and a spunisy heroine who fall love despite their bearts. The book about bealing from leorning forgivenes, selfieseness, charm and quirkty chamaters.
Cast sllctucee
similarity: 0.71
bacistmp
Figure 5: example for item generation task.
D.2 Example for Interaction Interpretation
In Section 4.4.2 we perform a real user study on interaction interpretation, which guides LLM to provide an explanation on why the user would like the item. We present case study in Figure 6 to simply illustrate the process. The descriptors of the user and items (including positive items and negative items in the test set) generated by our FACE framework, and the LLM is prompted to act as an interpretable recommender to provide an explanation for user-item interaction (prompt template in Appendix E The generated explanations are then evaluated by real users and LLMs. Ideally, the explanations provided by LLM for actual interactions (a user and its positive item) should be more reliable and persuasive, achieving high ranking.
The results show that the explanations of interactions between users and positive items are more credible than those between users and negative items. This demonstrates that the relevance between the descriptors of users and items can be effectively understood by L.LMs to generate reasonable explanations for user-item interactions.
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Rositive itmm: darkening, gloss, opus, nightfall, glint, resultant...
User: nightly, minisenes, uneasy, hushod, ambiguous, intriguing...
Negative Item:
holiday, postponed, cheerful, solemn, reliove, baggant...
Descriptors
LLM Reasoning
readec's fasctnation atmcopheric settings and unellling paratives nigus perfectly with book's dark whispered lones and mystedous, opulest aesthetic, creating immerive rxpertitnoo they would likely enjoy.
book's blend your taste for intriguing, ambigunus stories that balance unease with quact depth. It mimimo user's preference for namiiives that both hushed ussettling, much like gripping
yet solemn
appeal
Explanation
Real User Ranking
More Credible
Credible
Figure 6: An example for interaction interpretation.
E Prompt Template
For summary generation, the prompt template in the original paper of RLMRec.
For item-retrieval task, we use the following prompt template to search for the most relevant item from the list of candidates based the descriptors:
Prompt for Item Retrieval Task
### Instruction
Given a set descriptors an item, find the most relevant item from list of candidates. The
descriptors are a list of keywords that descrihe the item. The candidates are a list of items with their profiles.
Request:
Provide the the most relevant item
list candidates. For example, your reply 'iteml'. Do not provide any other information or explanation.
3. Try to find the association between the descriptors and the profiles.
### Input
The descriptors of the item <descriptors>. The candidates are: <candidate list>. Please provide id of the most relevant item from the list candidates.
For item generation task, we use the following prompt template to generate new item based on the descriptors given by FACE:
Prompt for Item Generation based on Descriptors
### Instruction
Based on the item descriptors, generate title and description of the item
1. The provided item is from <dataset name, e.g., Amazon> dataset, where the item is <dataset item,
e.g., book>.
The descriptors are a list of keywords that describe the item.
3. The title is not required to be an actual title, but it should be a title-like phrase.
4. The description should be one two sentences that introduce the item.
Please provide your answer JSON format, following this structure:
"title': "..",, 'description:: "..."
).
Examples:
<examples>.
### Input
The descriptors the item are: <descriptors>
In real user study, we generate the explanation for the user-item interaction based on the FACE descriptor sets or RLMRec profiles of the user-item pair. The LLM is prompted to act as a recom- mender and provide an explanation for the interaction. Specifically, we randomly select 40 users from Amazon-bout book dataset. For each user, randomly seleet item from their interacted items in
25

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the test set and 3 randomly selected items. In other words, we have 4 user-item pairs for each user: 1 positive pair and negative pairs. We generate the explanations for these pairs. Based on FACE descriptors, the prompt template is as follows:
Prompt for Interaction Interpretation based on Descriptors
#iift Instruction
Task: Act as an explanation generator for recommendation systems. Given pair of user(reader) and item(book) descriptor sets, need to generate short explanation of why the user would like the item. Note:
Assume that the user interact with (like) the item, whatever their descriptors are.
2. Provide your evidence to support the assumption based on the descriptors briefly, preferably in one or two sentences, in English.
3. You may need to focus the implicit connections between their descriptors' conceptual space. Based the descriptors' conceptual space, you may speculate assume what exactly the user preference the item genres is.
5. Your explanation is based on the descriptors and your speculation. Request:
1. Don't mention the name of the item (book title).
Don't try understand every deseriptor, just focus on the most relevant ones or the collective
meaning the descriptors.
3. Don't list the descriptors in the explanation, and don't use the word "descriptors".
4. Don't make general statements.
Just oulput the explanation in two sentences. No additional information is required, such reasoning processes, evidence or notes.
#il# Input
Generate the interaction explanation for the following user and item based on their descriptors.
User descriptors: <user descriptors>.
Item descriptors: <item descriptors>.
Based on RLMRec profiles, the prompt template is as follows:
Prompt for Interaction Interpretation based on RLMRec Profiles
#ilf Instruction
Act as an explanation generator for recommendation systems. Given pair of user and item profiles, you need to generate short explanation of why the user would like the item.
Requirements:
like) item. 2. Provide your evidence to support the assumption based on the profiles briefly, preferably in one or two sentences.
Assume that the user interact
Just explain the reason for the interaction, and don't ion't make general statements.
The given user and item are from Amazon dataset, where the user and item are reader and book respectively.
5. DON'T mention the item's 's name (i.e. the title of the book) in the explanation. Use "book" instead (without quotes).
### Input
Generate interaction explanation for the following user and item based on their profiles.
User profile: <user profile>.
Item profile: <item profile>.
After generating the explanation, we ask volunteers as well as LLMs evaluate the quality of the explanation. We design ranking setting where they are asked to rank the explanations for the user-item pairs. The ranking is based on the quality of the explanation, where is the best and 4 the worst. For LLM ranking, we prompt the LLM to act as recommender and provide a ranking for the explanations. The prompt template as follows (same for explanations based on FACE descriptors and RLMRec profiles):
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Prompt for I.M Ranking Explanations
#ill Instruction
Data Description:
each user corresponding to items, only item a positive sample that interacts with user, while the others are negative samples.
The explanation assumes that the user and the item will interatt, and is generated by an LLM based on the descriptors profiles of the and item.
Focus credibility of explanation to make judgments.
Task Requirement:
Rank the credibility of the interaction explanations for the 4 items of user, with ranks from to 4. The higher the rank, the more credible the explanation, and the more likely the user-item pair positive sample.
Output Format:
Output the four ranking numbers separated by spaces, without any other content, e.g., 2 3. ##f Input
For user
Item Explanation: <iteml explanation>
Item 2 Explanation:
<item2 explanation>
Item 3 Explanation:
<item3 explanation>
Item 4 Explanation:
<item4 explanation>
Please rank the explanations from I 4 based their credibility.
F Cold-start Experiments
Our framework provides an effective cold-start solution collahorative filtering models. In FACE, when using AutoEncoder for disentangled mapping, model simultancously trains decoder that generates original collaborative representations from descriptors. We can leverage textual information from the dataset generate descriptors for cold-start items. These descriptors can then be fed into the well-trained decoder to generate collaborative representations for cold-start items. The same principle applies to cold-star users.
We conducted an item zero-shot experiment to illustrate the model's cold-start performance: For given dataset, 1/5 of the items were designated as cold-start items and excluded during training. During testing, we utilized the textual summary information to allow the L.M generate descrip- tors, mimicking the association between textual information and descriptors of other items. The embeddings of these generated descriptors were then fed into the decoder to produce collaborative representations. Item recommendations were then made by calculating the similarity with user repre- sentations. We compared our approach against AlphaRec, model with strong cold-start performance, using the same base model and textual information. The results presented below:
Table 9: Cold-start performance comparison.
Dataset
Metric
AlphaRec
FACE
R @@5
0.0505
0.0630
0.1370
0. 0.1395
Amazon-book
N@5
0.0456
0.0611
N@20
0.0750
0.0912
R@5
0.0347
0.0342
R 20
0.1116
0.1010
Yelp
N@5
0,0368
0,0374
N@2
0.0621
0.0658
R@5
0.0447
0.0496
R @ 20
0.1287
0.1358
Steam
N N@5
0.0430
0.0562
N @
0.0729
0.0838
27

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G Complexity Analysis
Let B =|S]] denote the batch size, the number of disentangled descriptors, [D] the size of the codebook for quantization, and the embedding dimension. The disentanglement module, involving disentangled mapping and a Transformer encoder, has a time complexity of O (n 7 n)); The quantization module requires a nearest-neighhor search over the codebook, with a time complexity of O ((Bndd); The Alignment stage, using a Transformer- based language model and contrastive loss, has time complexity of O(B(ndd + df n) + B²d), Considering that the batch size B is much smaller than the total number of users and items N = [M[] + [Z], the additional computational cost of the FACE framework is linear with respect to N.
H Limitations
Due to the computational resource constraints during experimentation, the current system adopts relatively small-scale language models (e.g., Llama2-7B, MiniLM--6,, Qwen2-7B); the framework's performance when integrated with larger-scale language models remains unverified. While the effectiveness of the smaller embedding model has been empirically validated in various studies, we hypothesize that adopting models with expanded parameter sizes could further enhance text embedding quality and vocabulary representation capabilities. future work, we intend extend our methodology to larger open-source LLMs (e.g., LLaMA-3 series or 70B-scale architectures) to fully evaluate the potential of sentence embedding and semantic alignment. Besides, our framework is currently limited to the collaborative filtering domain, and its applicability to other recommendation scenarios (e.g.. sequence recommendation) remains unexplored. We plan investigate the extension of our framework to these domains in future research.
I Broader Impacts
Our proposed framework, FACE, aims to enhance the performance and interpretability of collaborative filtering recommendation systems via mapping CF embeddings into LLM tokens. It is model- agnostic framework that can be applied to various CF models and LLMs, making it versa tile and adaptable different recommendation scenarios. The interpretability aspect of FACE understand the reasoning behind recommendations, potentially increasing user trust and satisfaction. Beyond improving recommendation accuracy and interpretability, convert CF embeddings into LLM tokens can also facilitate more downstream tasks. For instance, the generated descriptors can be fed into LLMs to carry recommendation tasks, especially for cold-start scenarios, and descriptors can be modified specifically to achieve controllable recommendations.
help users
However, despite the advancements offered by FACE, it is essential to acknowledge the potential drawbacks. The reliance on LLMs may introduce biases present in the original training data, which could lead to skewed recommendations well the bias in the generated explanations. Additionally, the computational resources required for LLMs may limit the accessibility of this approach for smaller organizations researchers with limited resources.
28


Reviews:
[{"review_id": "VIEYR6cfqn", "reviewer": "Reviewer_dB6G", "summary": "The paper proposes a tokenization framework FACE, that bridges the gap between embedding-based representation derived from collaborating filtering methods and the discrete representations used in LLM embeddings. The method maps the CF embeddings into discrete LLM tokens referred to as descriptors. To capture multiple semantically meaningful concepts, the method leverages disentangled projections of the CF embedding and then learns a RQ-VAE tokenizer with frozen LLM embeddings as the codebook. In addition, a contrastive alignment objective is introduced for learning semantically meaningful user/item representations, allowing LLMs to understand items/users without additional LLM fine-tuning. Experiments across three different recommendation datasets depict the efficacy of the method proposed.", "questions": "- In Section 3.3.1, it is stated that we have summaries for both users and items. Are we doing contrastive loss for both items and users separately? Please clarify in the text? \n- For joint training, what is recommendation objective? Is it retrieval/ranking task? Do we optimize all the parameters of the LLM for this task?\n- Why is Residual Quantization used if we only consider the first code as the descriptor of the encoded disentangled CF embedding?\n- How does the method generalize in the cold-start setting for users and items?", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n- The proposal for doing end-to-end tokenization with LLM finetuning for recommendation tasks seems novel. \n- The paper is overall clear and easy to understand. \n\n**Weaknesses**\n\n*Lack of discussions around scalability*\n- The key desirable property for models in recommendation system is the ability to scale and to be able to operate in cold-start settings. However, the paper doesn't discuss this. How will the chosen LLM codebook scale to millions/billions of items for a real-world recommendation system corpus? Can the alignment of CF embeddings and LLM  embeddings help with generalization in cold-start settings?\n\n*Missing crucial baselines and citations*\n- Generative Retrieval for RecSys (Rajput et al. 2023): This paper first introduced learning Semantic ID for recommendation items using RQ-VAE for the generative retrieval task. This paper should be among the main baselines for the proposed item tokenization approach.\n- Traditional recommender systems (S3-Rec (Zhou et al. (2020)), SASRec (Kang et al. 2018)) are not considered as baselines.\n- SPAE tokenization SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs. This work also uses frozen LLM embeddings to learn hierarchical descriptors when learning multi-modal tokens. Please consider adding it to related work.\n\n*Few implementation details are unclear*\n- In Section 3.3.1, it is stated that we have summaries for both users and items. Are we doing contrastive loss for both items and users separately? Please clarify in the text? \n- For joint training, what is recommendation objective? Is it retrieval/ranking task? Do we optimize all the parameters of the LLM for this task?\n- Why is Residual Quantization used if we only consider the first code as the descriptor of the encoded disentangled CF embedding?\n\n*Unclear where the improvement is coming from*\n- It is not clear from the paper what is causing the improvement shown in Table 1. Is it because of the proposed tokenization framework or  because the LLMs already have enough knowledge about these chosen datasets? Perhaps LLMs already contain the collaborative knowledge of these open-sourced recommendation benchmarks?", "was_revised": true, "final_justification": "Some of my concerns have been addressed. I greatly appreciate the new cold-start experiment that were included to show that this method can be used for cold-start generalization. I am still unsure about the scalability arguments (as discussed in my comment). I still think the pros outweigh the cons. Hence, the rating is increased to 4.", "rebuttal": "Thank you for your valuable comments and for recognizing the novelty of our approach. \n\nHowever, first of all, we would like to clarify that the main contribution our proposed framework, FACE, is a **general** framework mapping collaborative embeddings from any CF model into pre-trained tokens from any LLM, which enhances **interpretability** as well as recommendation performance. This is the first general enhancement framework that enables LLMs to directly understand collaborative representations in the form of LLM tokens. Below are our responses to your concerns.\n\n> Response to W1 & Q4: scalability & cold-start\n\nThe chosen LLM codebook in our framework has good **scalability**, and is enough for practical recommendation scenario. Let $|\\mathcal{D}|$ denote the size of the LLM vocabulary, $n$ the number of disentangled descriptors, and $H$ the number of RQ layers. The number of possible token combinations reaches: $$C^n_{|\\mathcal{D}|} \\times (A^n_{|\\mathcal{D}|})^{H-1} = \\frac{(A^n_{|\\mathcal{D}|})^{H}}{A^n_n}$$ Considering a semantic-filtered vocabulary size of approximately 2000 tokens, with $n=16$ disentangled descriptors and $H=3$ layers, this number is estimated to be over $10^{100}$. Even for $H=1$, it is approximately $10^{34}$. This capacity is sufficient to represent items in real-world recommendation scenarios. For complex or information-rich real-world item/user spaces, the number of tokens mapped per item can be increased for further scalability. \n\nOur framework provides **an effective cold-start solution** for collaborative filtering models. However, in our paper, we primarily focus on semantic mapping and interpretability. We will incorporate this part in the revised version. Below, we present the analysis and experiment. In FACE, when using an AutoEncoder for disentangled mapping, our model simultaneously trains a decoder that generates original collaborative representations from descriptors. We can leverage textual information from the dataset to generate descriptors for cold-start items. These descriptors can then be fed into the well-trained decoder to generate collaborative representations for cold-start items. The same principle applies to cold-start users. \n\nWe conducted an item **zero-shot experiment** to illustrate the model's cold-start performance: For a given dataset, 1/5 of the items were designated as cold-start items and excluded during training. During testing, we utilized the textual summary information to allow the LLM to generate descriptors, mimicking the association between textual information and descriptors of other items. The embeddings of these generated descriptors were then fed into the decoder to produce collaborative representations. Item recommendations were then made by calculating the similarity with user representations. We compared our approach against AlphaRec, a model with strong cold-start performance, using the same base model and textual information. The results are presented below:\n\n| Dataset    | Metric |  AlphaRec  |    FACE    |\n| :--------- | :----: | :--------: | :--------: |\n| **Amazon** |  R@5   |   0.0505   | **0.0630** |\n|            |  R@20  |   0.1370   | **0.1395** |\n|            |  N@5   |   0.0456   | **0.0611** |\n|            |  N@20  |   0.0750   | **0.0912** |\n| **Yelp**   |  R@5   | **0.0347** |   0.0342   |\n|            |  R@20  | **0.1116** |   0.1010   |\n|            |  N@5   |   0.0368   | **0.0374** |\n|            |  N@20  |   0.0621   | **0.0658** |\n| **Steam**  |  R@5   |   0.0447   | **0.0496** |\n|            |  R@20  |   0.1287   | **0.1358** |\n|            |  N@5   |   0.0430   | **0.0562** |\n|            |  N@20  |   0.0729   | **0.0838** |\n\nAs shown, FACE facilitates a bidirectional mapping between collaborative filtering representations and LLM vocabulary, thereby **empowering traditional collaborative filtering models with cold-start capabilities**. It achieves better performance than AlphaRec, which relies on a mapping to initial collaborative representations. This demonstrates FACE's strong cold-start and scalability potential.\n\n> Response to W2: baselines & citations\n\nThank you for your suggestions. Our primary focus, as stated in the Limitations section, is on **collaborative filtering**, a mainstream and classic domain in recommendation, rather than sequential recommendation (e.g., TIGER (Rajput et al. 2023), S3-Rec (Zhou et al. 2020), SASRec (Kang et al. 2018)). Sequential recommendation operates under its own distinct problem settings. However, it's worth noting that our framework could be adapted for sequential recommendation tasks, for instance, by enhancing item embeddings of a sequential recommendation model or by leveraging LLMs for sequential modeling using the mapped descriptors that LLMs can understand, as discussed in Appendix G Broader Impact. We are keen to explore this in future work. \n\nRegarding \"SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs,\" this work is an image tokenization paper in the image-text multi-modal domain, whereas our work performs disentangled mapping in the collaborative filtering scenario. We will cite this paper and add it to our discussion.\n\n> Response to W3, Q1, Q2, & Q3: Implementation Details\n\n**Q1: Do contrastive loss for users and items separately or together?** We conduct contrastive loss for users and items **together** within a single batch, similar to the approach in CTRL (Li et al. 2023), which leads to stable and robust learning during experiments. Performing the loss separately is also a valid approach.  We will add this detail to the paper in revision.\n\n**Q2.1: What is recommendation objective?** Our proposed framework is a **model-agnostic plugin**. Therefore, the recommendation objective is that of the base model. In our experiments, the collaborative filtering base models all adopted the **BPR recommendation loss function as their objective** and were evaluated under the **all-ranking protocol** (calculating similarity between users and all candidate items). These settings are consistent with the original papers to ensure fair comparison.\n\n**Q2.2: Do we optimize all the parameters of the LLM?** In fact, our FACE achieves semantic alignment **without fine-tuning LLMs**, as  stated in our Abstract and Introduction. This is because we semantically map the CF embeddings directly to the LLM's vocabulary, which allows the LLM to understand the representations and leverage its pre-trained capabilities to align without any fine-tuning. Indeed, this represents a significant advantage. Compared to approaches that require fine-tuning, our method demonstrates superior efficiency and generality. We apologize for any misunderstanding regarding LLM fine-tuning, and will emphasize this point more clearly in the next version of the paper.\n\n**Q3: Why Residual Quantization (RQ) if we only consider the first code?** A large body of existing work has demonstrated that Residual Quantization (RQ) exhibits greater stability and superior performance compared to Vector Quantization (VQ). The hierarchical structure of RQ allows for different levels of semantic information to be captured, and summing across layers provides a fine-grained approximation of disentangled embeddings, as discussed in Section 3.2.2,  For interpretability, we used only the descriptors from the first layer in our interpretability experiments because they are most similar to the original disentangled embedding and contains the most salient semantic information. In our experiments, we found that using only the first layer for alignment already yielded excellent results.\n\n> Response to W4: Where the improvement is coming from? \n\nThe performance improvement comes from leveraging the pre-trained capabilities of large language models (e.g., world knowledge, reasoning abilities) to effectively align the tokens mapped from collaborative embeddings with textual information in the dataset, thereby injecting textual semantic knowledge. Existing alignment methods (such as RLMRec) perform direct cross-modal alignment, which fails to make good use of semantic logical relationships. Our framework maps collaborative filtering representations to LLM tokens, effectively leveraging the pre-trained knowledge within LLMs. We designed a contrastive alignment loss and employed a series of measures to ensure proper gradient propagation, thereby enhancing model performance through the injection of textual information. Our ablation studies (Section 4.6) demonstrate that the tokenization mapping component alone provides minimal or negligible improvement to the base model (as shown in the \"w/o align\" part of our ablation experiments). The significant performance gains are observed when the semantic alignment is introduced, proving the effectiveness of our model in the CF representation-to-LLM vocabulary mapping, which enables utilization of LLM knowledge."}, {"review_id": "fWR0uC7rT1", "reviewer": "Reviewer_kYKc", "summary": "The paper proposes FACE, a model-agnostic recommendation framework that combines a RQ-VAE and contrastive learning to map collaborative filtering embeddings into a set of pretrained LLM tokens. It aims to leverage the power of LLMs to improve both performance and interpretability.", "questions": "+ Just an extended question: can the proposed autoencoder-based bidirectional mapping strategy be extended to facilitate alignment across other modalities?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "The core idea of enabling direct interpretation of collaborative filtering representations by large language models addresses a fundamental challenge in the field. This approach establishes a bridge between traditional CF methods and modern LLMs, which could potentially unlock new possibilities for advanced recommendation tasks such as controllable generation, cross-domain transfer, and multi-modal recommendation scenarios.\n\nThe paper has the following strengths:\n\nS1: The proposed strategy of combining a quantized autoencoder for CF-LLM mapping while maintaining information integrity and contrastive learning for semantic alignment demonstrates solid technical grounding. The design choices appear well-motivated and the three-stage training strategy shows practical consideration for optimization stability.\n\nS2: The framework's model-agnostic nature represents a significant practical advantage. Rather than requiring specific architectural modifications, FACE can be applied to various existing CF models (GMF, LightGCN, SimGCL, etc.), which enhances its potential for widespread adoption and real-world deployment.\n\nS3: The direct mapping of embeddings into existing semantic tokens as descriptors offers an elegant solution to the interpretability challenge. This approach not only provides human-readable explanations but also enables LLMs to perform complex reasoning tasks based on these semantic representations, opening doors for sophisticated recommendation applications beyond simple ranking.\n\nS4: The experimental validation is comprehensive, demonstrating consistent improvements across multiple datasets and base models. The interpretability studies, particularly the real user evaluation comparing FACE descriptors with RLMRec profiles, provide convincing evidence that the mapped tokens capture meaningful semantic information while being more concise (16 tokens vs. paragraph-length profiles).\n\nThe paper may have the following weaknesses:\n\nW1: While the overall methodology is well-structured, the distinction between the latent variables $z_e$, $z_d$, and $z_q$ could be clarified further. For instance, by emphasizing the role of $z_d$ or providing a brief background on RQ-VAE to aid reader understanding.\n\nw2: Some text in Figure 1 is a bit small, which could be adjusted to improve readability for readers.\n\nOverall, I think this paper presents very interesting and beneficial work for recommendation systems. Based on the above analysis, I recommend acceptance of this paper.", "was_revised": true, "final_justification": "Since the authors have addressed my concerns, I have decided to maintain my positive score and recommend accepting this paper.", "rebuttal": "We sincerely thank you for your insightful feedback. We are particularly encouraged by your recognition of our work's significance: \"The core idea of enabling direct interpretation of CF representations by LLM addresses a fundamental challenge in the field.\" Below, we address your suggestions and questions. \n\n> On Clarification and Readability (W1, W2) \n\nWe appreciate your constructive suggestions regarding clarity. In the revised manuscript, we will incorporate the following changes: \n\n**Clarifying Variables in RQ**: We will expand Section 3.2.2 and the caption of Figure 1 to further clarify the roles of the latent variables.  \n\n+ $z_e$: The disentangled collaborative embedding derived from the CF model.\n\n+ $z_d$: The quantized embedding corresponding to a descriptor token from the LLM's vocabulary. This variable plays a pivotal role in achieving semantic alignment and interpretability, as the alignment loss is backpropagated through $z_d$.    \n+ $z_q$: The approximate disentangled collaborative embedding, constructed from the sum of different levels of quantization codewords.\n\n**Improving Figure Readability**: We will increase the font size of the text in Figure 1 to ensure better readability for all readers. \n\n> Can the proposed strategy be extended to facilitate alignment across other modalities? (Q1)\n\nThank you for this question. We believe the core principles of our framework are generalizable. Our method establishes a bidirectional mapping between continuous representations (from CF models) and discrete semantic tokens (from an LLM). The efficacy of this mapping is ensured by the autoencoder's reconstruction architecture and continuous-to-discrete quantization, while semantic alignment is achieved via contrastive learning. Crucially, we maintain effective gradient flow from the discrete token space back to the continuous embedding space by using a pseudo-inverse mapping. This set of techniques is not inherently limited to recommendation systems. It could potentially be adapted for other cross-modal alignment tasks, such as aligning continuous image embeddings with discrete textual tokens. This is an exciting direction for future work that we are keen to explore. Thank you again for your valuable time and thoughtful review."}, {"review_id": "kJvnPMIQ1A", "reviewer": "Reviewer_SnQC", "summary": "This paper presents **FACE**, a novel framework designed to bridge collaborative filtering (CF) models with large language models (LLMs) by mapping CF embeddings into LLM tokens. It introduces a disentangled projection module and a quantized autoencoder to convert CF embeddings into discrete LLM tokens. Additionally, a contrastive alignment objective is proposed to ensure semantic consistency throughout the process.", "questions": "See Weaknesses.", "limitations": "I believe this work does not pose potential negative societal impacts.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1. The methodology is well-structured and technically sound. The combination of disentangled projection, residual quantization, and contrastive learning reflects a thoughtful approach to addressing the complexities of mapping between different embedding spaces.\n\n2. The authors provide anonymized codes, which enhances the reproducibility of the work.\n\n3. Comprehensive experiments are conducted to effectively demonstrate the proposed method's efficacy.\n\n**Weaknesses**\n\n1. In Section 3.3.1, the description of the Descriptor Embedding is somewhat difficult to follow. It would be beneficial to provide a clearer explanation of \"descriptor embeddings,\" including the distinction between descriptor and summary embeddings. Additionally, clarification on the pseudo-inverse matrix and how it is obtained would be helpful.\n\n2. A more detailed time complexity analysis of the proposed framework would be advantageous.", "was_revised": true, "final_justification": "Based on the Strengths and the rebuttals, I suggest Accept for this paper.", "rebuttal": "We sincerely thank you for your recognition of our work, noting that “The methodology is well-structured and technically sound.” Here are our replies to your concerns. \n\n> W1.1: It would be beneficial to provide a clearer explanation of \"descriptor embeddings,\" including the distinction between descriptor and summary embeddings. \n\nThank you for your suggestions. **Summary embeddings** are derived from textual information in the dataset, and then processed by LLM enhancement and embedding. In contrast, **descriptor embeddings** originate from the collaborative embeddings. Specifically, a collaborative embedding is mapped to a set of discrete \"descriptors\" (LLM tokens), denoted as $z_d$. These tokens are then inserted into a prompt template to form a complete sentence, which is subsequently fed into the LLM to generate a final, semantically rich sentence embedding. We will provide a clearer explanation in the revised version.\n\n> W1.2: Clarification on the pseudo-inverse matrix and how it is obtained would be helpful.\n\nRegarding the **pseudo-inverse matrix**: during quantization, we obtain the token embedding $c$ (i.e., $z_d$) after dimensionality reduction. It is required to recover the original token embedding $c_0$ while maintaining the gradient for the alignment stage. Considering the projection matrix $W_c \\in \\mathbb{R}^{d \\times d_{LLM}}$, since $d<d_{LLM}$,, a standard inverse matrix does not exist. Therefore, we employ the pseudo-inverse to solve this problem. From the relation $c=W_cc_0$, **we can derive the solution for $c_0$ as**:\n\n$$c_0 =(W_c^TW_c)^{−1}W_c^T c$$ ,\n\nwhere we denote $(W^T_cW_c)^{-1}W_c^T$ as the pseudo-inverse matrix $W_c^{-1}$. While computing, this requires $W_c$ to have full row rank (to ensure $W_c^T W_c$ is invertible), this condition can be met in practice by adding a small perturbation to the weights for numerical stability. We really appreciate your suggestions, and will provide a clearer explanation in the revised version.\n\n> W2: A more detailed time complexity analysis of the proposed framework would be advantageous.\n\nThank you for your valuable suggestions. Let $B$ denote the batch size, $D$ the number of disentangled descriptors, $C$ the size of the codebook for quantization, and $d$ the embedding dimension. \n\n+ The **Disentanglement module**, involving a disentangled mapping and a Transformer encoder, has a time complexity of $O(B(D^2d + d^2D))$. \n+ The **Quantization module** requires a nearest-neighbor search over the codebook, with a time complexity of $O(BCDd)$. \n+ The **Alignment stage**, using a Transformer-based language model and contrastive loss, has a time complexity of $O(B(D^2d + d^2D) + B^2d)$.\n\nConsidering that the batch size $B$ is much smaller than the total number of users and items $N$, the additional computational cost of the FACE framework is linear with respect to $N$. This is important in practical scenario where $N$ grows rapidly, which shows the efficiency of our proposed framework."}, {"review_id": "1zos9Pr9St", "reviewer": "Reviewer_yz2S", "summary": "This paper proposes FACE, a general framework for mapping collaborative filtering (CF) embeddings into interpretable LLM tokens to bridge the gap between non-semantic CF representations and LLM understanding. The motivation stems from the challenge that LLMs cannot directly interpret latent CF embeddings, limiting their effectiveness in recommendation systems. The methodology involves two main components: (i) a disentangled projection module that decomposes CF embeddings into concept-specific vectors, and (ii) a quantized autoencoder that converts continuous embeddings into discrete LLM tokens (descriptors) using residual quantization with a frozen LLM vocabulary as codebook. A contrastive alignment objective ensures semantic consistency between mapped tokens and textual signals. Experiments on three datasets demonstrate consistent performance improvements across various CF models, with interpretability studies confirming that the generated descriptors can be understood by LLMs.", "questions": "1. Could the authors provide more theoretical or empirical analysis on how the semantic alignment objective (contrastive learning) directly contributes to recommendation accuracy improvements, beyond just enabling interpretability? The connection between these dual objectives needs clearer justification.\n\n2. How does FACE compare with other recent LLM-enhanced recommendation methods beyond RLMRec? Additionally, could the authors discuss the computational overhead and scalability of the three-stage training process compared to simpler alternatives?\n\n3. Can the trained FACE framework be transferred across different domains or datasets without retraining? What are the limitations regarding domain adaptation, and how sensitive is the method to the quality and availability of textual features in new datasets?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "**Strengths:**\n- The paper targets a useful and interesting research task in LLM-enhanced recommendation. How can we translate hidden embeddings given by collaborative filtering models into understandable LLM tokens? The paper decomposes this task into multiple steps: i) disentangling CF embeddings into multiple embeddings related to specific semantic aspects, and ii) quantizing the embeddings into LLM tokens. The framework design is elegant and intuitive, addressing a fundamental challenge in bridging CF and LLM modalities. This design not only greatly improves the explainability of recommendation models, but also provides a better alignment with textual features to improve recommendation accuracy.\n\n- The novelty of the proposed method is notable, particularly in the quantization approach that uses frozen LLM vocabulary as codebook combined with residual quantization for hierarchical semantic representation. The disentanglement method through orthogonally initialized multi-projectors followed by transformer encoding is also innovative. The integration of these components with contrastive alignment learning represents a significant technical contribution that enables end-to-end learning of semantic mappings.\n\n- The proposed method provides consistent performance improvements for recommendation models of different categories, demonstrating its model-agnostic nature and general applicability across various CF architectures from matrix factorization to graph neural networks.\n\n- The real user study effectively demonstrates the interpretability of the proposed method, with both item retrieval and generation tasks showing that LLMs can understand and utilize the generated descriptors, and human evaluation confirming the quality of interaction explanations.\n\n- The paper is well-written with clear motivation, comprehensive methodology description, and thorough experimental evaluation. The technical details are presented clearly, and the experimental setup is comprehensive with proper ablation studies and hyperparameter analysis.\n\n**Weaknesses:**\n- The proposed method is mainly focused on improving the explainability of recommendation models, but the experiments are mainly designed to evaluate recommendation accuracy. There is a gap in the former part of the paper to encounter the connection between these two objectives.\n\n- The proposed method only involves one LLM-enhanced recommendation model (i.e. RLMRec) in the experiments, limiting the comparison with other LLM-based recommendation approaches and making it difficult to assess the relative advantages of FACE over alternative LLM integration strategies.\n\n- The proposed method seems reliant on the training dataset (textual features and the results of CF training on this dataset). It is not clear how the trained mapping functions can be applied to other datasets or domains, raising questions about the transferability and generalizability of the learned mappings.", "was_revised": true, "final_justification": "The authors have further addressed my concerns mentioned in the weakness part. I believe this is a clear accept paper given its novelty.", "rebuttal": "We sincerely thank you for your recognition of our work, and we are encouraged by your recognition that “The novelty of the proposed method is notable”. Here are our replies to your concerns. \n\n> Response to W1: About dual objectives.\n\nWe are the first to propose a novel semantic mapping method that creatively maps collaborative filtering representations into LLM tokens. This direct mapping improves explainability as well as accuracy. **The dual objectives of improving explainability and accuracy are not only the results of this semantic mapping but also serve to validate its effectiveness in turn.** The improvement in recommendation performance provides initial evidence that the mapped tokens can be understood by the LLM, which leverages its textual knowledge to enhance model performance. Furthermore, we conducted extensive experiments, including item generation/retrieval and real-user ranking studies, to further demonstrate that the descriptors and the relationships between user/item descriptors can be comprehended by the LLM, which enhances the interpretability of the embedding-based collaborative filtering method.\n\n> Response to Q1: How the semantic alignment objective (contrastive learning) directly contributes to recommendation accuracy improvements?\n\nRegarding the direct contribution of semantic alignment to accuracy, as demonstrated in RLMRec (Xi et al., 2023), maximizing the \"hidden prior belief\" beneficial for recommendation is equivalent to maximizing **the mutual information between the CF embedding and the semantic representation**. Our method can be viewed as a specific approach to maximizing this mutual information. Given the design of our semantic alignment, the alignment loss signal can be learned by the initial CF embeddings, thereby improving recommendation performance.\n\nEmpirically, our **ablation studies** in Section 4.6 (the \"w/o align\" variant) confirms that the accuracy improvement stems from semantic alignment. Additionally, the **hyperparameter sensitives** in Section 4.5 on the weight of the alignment loss shows that this parameter controls the intensity of semantic alignment (i.e., the amount of textual information injected). The recommendation metrics exhibit a trend of first increasing, linking the alignment objective to performance gains, and then decreasing as this coefficient rises, demonstrating too much information injection affects recommendation.\n\n> Response to W2 & Q2: How does FACE compare with other recent LLM-enhanced recommendation methods beyond RLMRec?\n\nWe have conducted new experiments comparing FACE with other recent LLM-enhanced recommendation methods:  CTRL(Wang et al. 2023) leverages contrastive learning for fine-grained knowledge alignment and integration with collaborative and language model; KAR (Xi et al., 2023) combine LM representations with traditional recommenders using a hybrid-expert adaptor. We compared all methods using the same textual embeddings and LightGCN as the backbone. The new experimental results demonstrate that: 1. FACE achieves superior performance compared to other LLM-enhanced recommendation methods; 2. FACE can be effectively applied to various existing LLM-enhanced recommendation approaches to further improve their performance.  These findings are consistent with those presented in our original paper, that proves the advantage of FACE.\n\n|                   | Amazon     |            |            |            | Yelp       |            |            |            | Steam      |            |            |            |\n| ----------------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |\n|                   | R@5        | R@20       | N@5        | N@20       | R@5        | R@20       | N@5        | N@20       | R@5        | R@20       | N@5        | N@20       |\n| LightGCN          | 0.0659     | 0.1563     | 0.0657     | 0.0961     | 0.0421     | 0.1141     | 0.0488     | 0.0726     | 0.0530     | 0.1361     | 0.0584     | 0.0862     |\n| **LightGCN+FACE** | 0.0705     | **0.1622** | 0.0705     | 0.1009     | 0.0446     | 0.1203     | **0.0519** | 0.0766     | 0.0559     | 0.1439     | 0.0611     | 0.0912     |\n| RLMRec            | 0.0669     | 0.1572     | 0.0663     | 0.0981     | 0.0426     | 0.1165     | 0.0495     | 0.0737     | 0.0545     | 0.1408     | 0.0599     | 0.0887     |\n| **RLMRec+FACE**   | 0.0679     | 0.1581     | 0.0672     | 0.0985     | 0.0435     | 0.1196     | 0.0503     | 0.0755     | 0.0556     | 0.1432     | 0.0604     | 0.0901     |\n| CTRL              | 0.0685     | 0.1566     | 0.0666     | 0.0963     | 0.0413     | 0.1161     | 0.0471     | 0.0719     | 0.0531     | 0.1375     | 0.0586     | 0.0865     |\n| **CTRL+FACE**     | 0.0699     | 0.1612     | 0.0706     | **0.1013** | 0.0432     | 0.1189     | 0.0510     | 0.0755     | **0.0569** | **0.1461** | **0.0620** | **0.0922** |\n| KAR               | 0.0671     | 0.1547     | 0.0660     | 0.0952     | 0.0408     | 0.1132     | 0.0476     | 0.0712     | 0.0502     | 0.1322     | 0.0553     | 0.0826     |\n| **KAR+FACE**      | **0.0712** | 0.1559     | **0.0714** | 0.1002     | **0.0446** | **0.1249** | 0.0518     | **0.0781** | 0.0516     | 0.1349     | 0.0571     | 0.0852     |\n\n> Response to Q2: Discuss the computational overhead and scalability of the three-stage training process compared to simpler alternatives.\n\nAt first, we conduct a time complexity analysis of the proposed framework. Let $B$ denote the batch size, $D$ the number of disentangled descriptors, $C$ the size of the codebook for quantization, and $d$ the embedding dimension. The disentanglement module, involving a disentangled mapping and a Transformer encoder, has a time complexity of $O(B(D^2d + d^2D))$; The quantization module requires a nearest-neighbor search over the codebook, with a time complexity of $O(BCDd)$; The Alignment stage, using a Transformer-based language model and contrastive loss, has a time complexity of $O(B(D^2d + d^2D) + B^2d)$.\n\nConsidering that the batch size $B$ is much smaller than the total number of users and items $N=|\\mathcal U|+|\\mathcal I|$, **the additional computational cost of the FACE framework is linear with respect to $N$**. Additionally, the number of descriptors $D$ is typically set to a small constant, the actual increase in computational load is modest.\n\n**The three-stage training strategy was designed not only for better convergence but also to reduce computational time.** In the first stage, we train the base model. The simple yet effective LightGCN model has a training complexity of $O(L⋅|\\mathcal E|⋅d)$, where $|\\mathcal E|$ is the number of interactions and $L$ the number of convolution layers. In the second stage, we only combine the AutoEncoder, without alignment. The full framework is trained in the final stage, which converges in just a few epochs due to effective pre-training—significantly faster than training the whole model from scratch. The time complexity of all the three stage is linear with respect to $|\\mathcal U|, |\\mathcal I|$ or $|\\mathcal E|$ and is efficient in practical scenario where they grow rapidly.\n\nFor comparison, RLMRec based on LightGCN, introduces an additional complexity of $O(Bd^2 + B^2d)$, quadratic with respect to the batch size $B$, but the overall training overhead remains linear with respect to the total number of users/items $N$, similar to our FACE framework.\n\n> Response to W3 & Q3: Can the trained FACE framework be transferred across different domains or datasets without retraining?\n\nThank you for this insightful question regarding the framework's transferability. We propose a model-agnostic framework that can be integrated with various collaborative filtering methods. The transferability of FACE is contingent upon the capabilities of the underlying base model. If the base model supports cross-domain recommendation, FACE may inherit this transferability. We will continue to explore the performance of the FACE framework in cross-domain settings in our future work."}, {"review_id": "CvUuTwYQWg", "reviewer": "Reviewer_YJtU", "summary": "This paper aims to bridge the semantic gap between the collaborative filtering models and the large language models. The authors proposed to map the CF ID embeddings into the semantic tokens of LLMs, with the following novelties: a disentangled projection module, a vector quantized variational autoencoder, and a contrastive alignment loss.", "questions": "1. The authors aim to achieve disentanglement when mapping the CF representation to the vocabulary space by employing \"orthogonally initialized\" projection heads. However, I'm not sure whether this (orthogonally initialized heads) is enough. These initially orthogonal heads may not become as orthogonal after the model optimization. I agree that disentangling these heads is beneficial, and I wonder whether it's better to achieve this goal by a more direct approach: employing a disentanglement loss (such as the de-correlation loss [1,2]) upon the outputs of these heads. \n\n2. In the \"Residual quantization\" paragraph, the authors propose to \"shares the same codebook across all quantization levels, with this codebook being composed of pre-trained LLM token embeddings\". However, I'm not sure whether this is the best choice. The codebooks and \"their embeddings\" in RQ-VAE should contain a hierarchical structure of the data, while the LLM token embeddings don't. More experiments or analysis on justifying the claim would be great.\n\n[1] Barlow twins: Self-supervised learning via redundancy reduction. PMLR, 2021.\n\n[2] Enhancing CTR Prediction with De-correlated Expert Networks. 2025.", "limitations": "1. Performance Evaluation. There are many existing works to inject LLM into recommendations(here I list several works for sequential recommendation:  [1,2,3], there should be many more for the general collaborative filtering/recommendation domain). In the performance evaluation, the authors should prove the effectiveness of the proposed approach by comparing it with these \"LLM4Rec\" methods, instead of just showing that FACE can enhance the performance of SOTA collaborative filtering models, because it's already well known that involving LLMs can boost the performance of CF. The authors' finding that \"the gains achieved by the PLMRec model integrated with the FACE framework are minimal\" precisely reflects my above-mentioned concern: existing work can already combine the LLM well, if so, then what's the advantage of FACE itself?\n\n2. In-depth Analysis. Besides the performance evaluation, the author should provide more in-depth analysis on the advantages of the proposed approach over existing works. For example, does FACE align the two spaces better than existing works[1]?\n\n3. Justification of the modules. The authors proposed several novel modules in Sec. 3.2 and 3.3. It would be great to justify them by: 1. What's the challenge the authors would like to tackle? 2. To what extent do existing approaches or naive methods resolve it? 3. Can the proposed module do better? How to measure or quantify it?\n\n[1] CTRL: ConnectCollab- orative and Language Model for CTR Prediction. 2023.\n\n[2] Enhancing Taobao Display Advertising with Multimodal Representations: Challenges. 2024.\n\n[3] DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation. KDD 2024.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 1, "originality": 2, "strengths_and_weaknesses": "- Motivation. The authors aims to bridge the gap between the collaborative filtering and large language models. This is an interesting and critical research problem.\n\n- Presentation. The whole paper is written well and is easy to follow.\n\n- Reproduction. The authors provide the released code on the proposed FACE model for reproduction.", "was_revised": true, "final_justification": "The authors provided detailed responses to address my questions regarding performance evaluation, analysis, and justification of the disentangled heads and codebooks. After resolving these concerns, I'm convinced of the novelty and the technical contribution of this paper.", "rebuttal": "We appreciate your comments. However, we need to emphasize that the main motivation and contribution of our paper. \n\nWe are the first to **map hidden embeddings given by collaborative filtering methods into pre-trained LLM tokens**, in order to improve the **interpretability** of arbitrary traditional CF models, and to this end, we propose a general and interpretable semantic mapping framework. This framework enables large language models to understand recommendation embeddings through semantic mapping, thereby improving interpretability as well as recommendation performance by leveraging LLM knowledge. For this, we have designed extensive experiments to demonstrate the quality of the mapped tokens and the interpretability beyond recommendation performance.\n\n> Response to Q1:\n\nThank you for your suggestions. In fact, orthogonally initialization is enough for the projection heads. Our model inherently tends to capture comprehensive preferences, automatically forcing disentanglement heads to capture different aspects, as the multiple mapped descriptors are required to reconstruct the original representation and align with semantic information. For validation, we design an experiment. We have implemented the de-correlation loss from the your listed method [2] to constrain the outputs of the multi-projectors. The result could be because it is difficult to balance this loss with the other losses, or this additional constraint hinders the semantic alignment.\n\n|                | Amazon     |            |            |            | Yelp       |            |            |            |\n| -------------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |\n|                | R@5        | R@20       | N@5        | N@20       | R@5        | R@20       | N@5        | N@20       |\n| de-correlation | 0.0698     | 0.1609     | 0.0696     | 0.0993     | 0.0435     | 0.1184     | 0.0507     | 0.0749     |\n| FACE           | **0.0705** | **0.1622** | **0.0705** | **0.1009** | **0.0446** | **0.1203** | **0.0519** | **0.0766** |\n\n> Response to Q2:\n\nIn the original paper of RQ-VAE (Lee et al. 2022), the author proposed and adopted shared codebook across all quantization levels. This technique maximize the utilization of codebook, making \"all code embeddings available for every quantization depth\". And we follow this setting in our work. For our work, although LLM token embeddings do not have an explicit hierarchical structure, the vector arithmetic of their embeddings is meaningful (e.g., king - man + woman ≈ queen). When obtaining the final quantized vector $z_q = \\sum_{i=1}^H c^{(h)}$, this property can be leveraged. Additionally, due to the nature of residual quantization, the first-level quantization result for each disentangled aspect captures the most significant information, which is then used for the interpretability experiments. For example, SPAE (Yu et al., 2023)  in the image-text multi-modal domain, also employs the same LLM vocabulary for its multi-level quantization.\n\n> Response to L1:\n\nFirst, to the best of our knowledge, our work is the first to map collaborative representations to **pre-trained**, **fixed** semantic tokens in the LLM for interpretability and is **not merely for improving recommendation performance using textual information**. Such a general framework also holds significant potential for many downstream tasks, such as conducting interpretable analysis, generating embeddings for cold-start items using the decoder, or leveraging the controllable recommendation capabilities of LLMs after mapping users and items to tokens.\n\nSecond, our framework can be applied to RLMRec, a collaborative filtering model that already incorporates textual information, to achieve further improvements in performance, even if the improvement is less compared to its application on traditional recommendation models that do not use textual information. This is the reason why we wrote, \"the gains achieved by RLMRec model integrated with FACE are minimal,\" and we apologize if our imprecise wording caused any misunderstanding.\n\nTo fairly compare the ability of different frameworks to align with large language models, it is necessary to conduct experiments under the same base model (LightGCN) and with the same textual information. We have included the method you listed, CTRL (Wang et al., 2023), which aims at CTR prediction in collaborative filtering. Additionally, we have included another enhancement framework that utilizes large models and text, KAR (Xi et al., 2023), and we simultaneously investigate the effect of further applying FACE to these LLM-enhanced recommendation models.\n\n|                   | Amazon     |            |            |            | Yelp       |            |            |            | Steam      |            |            |            |\n| ----------------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |\n|                   | R@5        | R@20       | N@5        | N@20       | R@5        | R@20       | N@5        | N@20       | R@5        | R@20       | N@5        | N@20       |\n| LightGCN          | 0.0659     | 0.1563     | 0.0657     | 0.0961     | 0.0421     | 0.1141     | 0.0488     | 0.0726     | 0.0530     | 0.1361     | 0.0584     | 0.0862     |\n| **LightGCN+FACE** | 0.0705     | **0.1622** | 0.0705     | 0.1009     | 0.0446     | 0.1203     | **0.0519** | 0.0766     | 0.0559     | 0.1439     | 0.0611     | 0.0912     |\n| RLMRec            | 0.0669     | 0.1572     | 0.0663     | 0.0981     | 0.0426     | 0.1165     | 0.0495     | 0.0737     | 0.0545     | 0.1408     | 0.0599     | 0.0887     |\n| **RLMRec+FACE**   | 0.0679     | 0.1581     | 0.0672     | 0.0985     | 0.0435     | 0.1196     | 0.0503     | 0.0755     | 0.0556     | 0.1432     | 0.0604     | 0.0901     |\n| CTRL              | 0.0685     | 0.1566     | 0.0666     | 0.0963     | 0.0413     | 0.1161     | 0.0471     | 0.0719     | 0.0531     | 0.1375     | 0.0586     | 0.0865     |\n| **CTRL+FACE**     | 0.0699     | 0.1612     | 0.0706     | **0.1013** | 0.0432     | 0.1189     | 0.0510     | 0.0755     | **0.0569** | **0.1461** | **0.0620** | **0.0922** |\n| KAR               | 0.0671     | 0.1547     | 0.0660     | 0.0952     | 0.0408     | 0.1132     | 0.0476     | 0.0712     | 0.0502     | 0.1322     | 0.0553     | 0.0826     |\n| **KAR+FACE**      | **0.0712** | 0.1559     | **0.0714** | 0.1002     | **0.0446** | **0.1249** | 0.0518     | **0.0781** | 0.0516     | 0.1349     | 0.0571     | 0.0852     |\n\nThe experimental results demonstrate that: 1. FACE achieves superior performance compared to other LLM-enhanced recommendation methods, meaning that LightGCN, which does not inherently use semantic information, outperforms existing LLM4Rec methods when integrated with FACE; 2. FACE can be effectively applied to various existing LLM-enhanced recommendation approaches to further improve their performance. These findings are consistent with those presented in the original paper, highlighting the advantages of the FACE framework in utilizing semantic mapping to enable LLMs to comprehend collaborative embeddings.\n\n> Response to L2:\n\nOur work focuses on mapping hidden embeddings given by collaborative filtering methods into interpretable LLM tokens, which to our knowledge has not been explored. Existing LLM4Rec alignment works directly align collaborative space with textual space by contrastive learning merely for textual injection and performance improvement (RLMRec, CTRL, etc.), while our work enables LLM to interpret collaborative embeddings and improve interpretability. Therefore, besides the performance evaluation, we have designed various interpretability experiments (Section 4.4) to demonstrate the quality and the application of semantic mapping.  For interpretability, we design item-retrieval, item-generation tasks and real-user study on LLM interpretation on interactions. These studies quantify the quality of semantic mapping, and suggest that our method constructs effective semantic mapping and improve the interpretability of latent CF embedding.\n\n> Response to L3:\n\n+ **What's the challenge the authors would like to tackle?** The challenge we aim to tackle is the semantic interpretability of user and item embeddings in traditional collaborative filtering models. To address this, we propose a general semantic mapping framework that is applicable to any CF model and any LLM.\n\n+ **To what extent do existing approaches or naive methods resolve it?** Traditional models do not address this issue at all. Most existing interpretable methods use language models to explain *interaction outcomes*, but they do not focus on providing interpretability at the embedding level. Knowledge-enhanced methods, on the other hand, typically leverage semantics solely to improve the recommendation performance of collaborative filtering, not for interpretability.\n\n+ **Can the proposed module do better? How to measure or quantify it?** Given that we are the first to tackle the semantic interpretability of user and item embeddings in traditional collaborative filtering models, besides recommendation performance evaluation, we designed various experiments to specifically quantify interpretability. These include item-retrieval and item-generation tasks, as well as a real-user study on LLM interpretation of interactions. These studies quantify the quality of the semantic mapping and suggest that our method improves the interpretability of latent CF embeddings by constructing this mapping."}]

Author rebuttal:

Meta-review: This paper introduces FACE, a framework for mapping collaborative filtering embeddings into discrete, interpretable LLM tokens. The reviewers reached a strong positive consensus, highlighting the practical advantage of its LLM-tuning-free design, and the demonstrated improvements in both performance and interpretability.

However, I find reviewers have missed critical related work the paper fails to discuss, compare, or position itself against. These include recent works, e.g., Demystifying Embedding Spaces [Tennenholtz et al., 2024]; Embedding-Aligned Language Models [Tennenholtz et al., 2025]; Factual and Tailored Recommendation Endorsements [Jeong et al., 2024]; and Item-Language Model [Yang et al., 2024]. These papers also address the critical challenge of making latent recommendation embeddings interpretable or usable by LLMs, albeit through different methods like direct embedding injection via adapters or reinforcement learning. This omission significantly weakens the paper's claims of novelty in addressing this general problem and reflects an incomplete survey of the immediate research landscape.

While this is a serious flaw, the core technical method in FACE—using a quantized autoencoder to map embeddings to a set of existing, human-readable LLM tokens—remains distinct from the approaches in the uncited literature. Given this technical distinction and the unanimous post-rebuttal support from all four reviewers, the paper is considered borderline. The final recommendation is to accept, contingent on the authors performing a revision of the introduction and related work sections. The camera-ready version must thoroughly discuss the concurrent literature, clearly articulate the unique contributions of FACE's mapping-based approach, and properly contextualize its place within the broader effort to bridge the gap between recommender systems and LLMs.
Decision: Accept (poster)