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1. Bibliographic Information

Title: 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, and Yanyong Zhang.
Affiliations: The authors are affiliated with several prominent academic and research institutions, including the University of Science and Technology of China, Hong Kong University of Science and Technology (Guangzhou), the Chinese Academy of Sciences, and Nanjing University of Aeronautics and Astronautics. This suggests a strong academic background in data science, artificial intelligence, and recommender systems.
Journal/Conference: The paper is available as a preprint on arXiv. An arXiv preprint is a preliminary version of a paper that has not yet undergone formal peer review for publication in a conference or journal. However, it is a standard way for researchers in fields like AI to disseminate their work quickly.
Publication Year: The paper references works from 2024 and 2025, suggesting it was likely submitted in late 2024 or early 2025.
Abstract: The paper addresses the challenge of integrating Large Language Models (LLMs) with Collaborative Filtering (CF) based recommender systems. The core problem is that LLMs cannot naturally interpret the latent, non-semantic vector embeddings produced by CF models. The authors propose FACE, a framework that maps these CF embeddings into discrete, pre-trained LLM tokens (which they call descriptors). This is achieved through a disentangled projection module and a quantized autoencoder. A contrastive alignment objective ensures these tokens are semantically meaningful by aligning them with textual information. The framework is model-agnostic, enhances recommendation performance without needing to fine-tune the LLM, and improves the interpretability of the recommendations.
Original Source Link:
- Official Source: https://arxiv.org/abs/2510.15729v1
- PDF Link: https://arxiv.org/pdf/2510.15729v1.pdf
- Publication Status: The provided arXiv ID 2510.15729v1 appears to use a future date format (YYMM), which is not standard for arXiv. This is likely a placeholder or a typo in the prompt. Assuming it's a recent submission, it is currently a preprint awaiting peer review.

2. Executive Summary

Background & Motivation (Why):
- Core Problem: Collaborative Filtering (CF) models are the backbone of modern recommender systems, creating powerful user and item embeddings. However, these embeddings are abstract mathematical vectors (latent representations) that lack explicit semantic meaning. Large Language Models (LLMs), which excel at understanding and reasoning with natural language, cannot directly interpret these CF embeddings.
- Importance: This "semantic gap" prevents the full potential of LLMs—such as their advanced reasoning and explanation capabilities—from being applied to enhance recommendations. Prior attempts to bridge this gap either ignored collaborative information (relying only on text) or used simple projection networks to align continuous embedding spaces, a method that doesn't allow a frozen LLM to truly "read" and understand the CF information.
- Fresh Angle: The paper introduces a novel approach: instead of mapping CF embeddings to another continuous vector space, FACE maps them directly to a sequence of discrete, pre-existing LLM vocabulary tokens. This transforms the abstract CF vector into a set of human-readable words (descriptors) that an LLM can natively understand, unlocking new possibilities for interpretation and complex tasks without costly LLM fine-tuning.
Main Contributions / Findings (What):
- A Model-Agnostic Framework: The paper proposes FACE, the first general framework that can take embeddings from any CF model and translate them into pre-trained LLM tokens. This makes it a versatile "plug-in" solution.
- A Novel Mapping Mechanism: FACE uses a combination of techniques—disentangled projection, a quantized autoencoder with a custom codebook, and residual quantization—to convert continuous, entangled CF vectors into a set of discrete, semantically rich tokens.
- Enhanced Performance and Interpretability: By aligning these generated tokens with actual textual descriptions using contrastive learning, FACE not only improves the recommendation accuracy of the base CF models but also makes the recommendations interpretable. Experiments show that an LLM can use these descriptors to retrieve items, generate descriptions, and explain user-item interactions.

Foundational Concepts:
- Collaborative Filtering (CF): A fundamental technique in recommender systems that predicts a user's interests by collecting preferences from many users ("collaborative"). It works on the principle that if person A has the same opinion as person B on an issue, A is more likely to have B's opinion on a different issue. Modern CF methods like Matrix Factorization or LightGCN learn latent vector representations (embeddings) for users and items from their interaction history.
- Embeddings: These are dense, low-dimensional vector representations of entities like users, items, or words. CF embeddings capture latent behavioral patterns (e.g., users who bought item X also bought item Y) but are not inherently semantic (the dimensions of the vector don't correspond to understandable features like "genre" or "brand").
- Large Language Models (LLMs): AI models like LLaMA or GPT trained on vast amounts of text data. They process information in the form of tokens, which are discrete units of text (words or sub-words). Each token in an LLM's vocabulary has a corresponding embedding.
- Vector Quantized Autoencoder (VQ-VAE): A type of generative model with an encoder and a decoder. The key innovation is in the middle: the encoder maps an input to a continuous latent vector, which is then "quantized" by finding the closest vector from a finite set called a codebook. This forces the model to represent the input using a discrete code, similar to how language uses a finite vocabulary. FACE adapts this idea by using LLM token embeddings as its codebook.
- Contrastive Learning: A self-supervised learning technique. Its goal is to learn representations by pulling embeddings of "positive pairs" (semantically similar items) closer together in the embedding space, while pushing embeddings of "negative pairs" (dissimilar items) farther apart.
Previous Works:
- LLMs as Standalone Recommenders: Early methods like TALLRec treated recommendation as a natural language task. They would feed item titles or descriptions directly into an LLM and prompt it to make a recommendation. Limitation: These methods often ignore the crucial collaborative signal (user-item interaction patterns) and struggle to outperform traditional CF models.
- Aligning Continuous Embedding Spaces: More recent works like RLMRec and ELM try to bridge the gap by training a lightweight adapter (e.g., a simple neural network) to map CF embeddings into the LLM's continuous embedding space. Limitation: The resulting mapped vectors are still just points in a high-dimensional space. They are not "native" LLM tokens, so a frozen LLM cannot easily interpret their meaning without being specifically fine-tuned or prompted in complex ways.
- Textualizing Collaborative Information: BinLLM attempted to represent collaborative information as a text-like string (e.g., an IP address format). Limitation: Using purely numerical tokens limits semantic understanding and still requires the LLM to be fine-tuned to grasp the new "language."
Differentiation: FACE distinguishes itself from all prior work by being the first to map CF embeddings to actual, semantic LLM tokens from a pre-trained vocabulary. Unlike alignment methods, this produces a discrete, human-readable output (descriptors). This allows any pre-trained, frozen LLM to directly "read" and reason about user preferences captured by a CF model, a significant step towards true interpretability and synergy between the two domains.

4. Methodology (Core Technology & Implementation)

The core of FACE is a two-stage process that transforms a continuous CF embedding $e$ into a set of discrete LLM tokens (descriptors) and ensures these tokens are semantically meaningful.

Figure 1: The overall architecture of our FACE framework. (a) Mapping stage. We employ a framework similar to the RQ-VAE architecture for the final embedding of a CF model with a frozen LLM codebook,… 该图像是论文中图1的示意图，展示了FACE框架的整体架构，包括(a)映射阶段，采用类似RQ-VAE架构将协同过滤(CF)嵌入编码为预训练大语言模型(LLM)的离散tokens；(b)对齐阶段，通过对比学习实现描述符与文本摘要的语义对齐。

As shown in Figure 1, the architecture consists of a Mapping Stage (a) and an Alignment Stage (b).

Principles: The central idea is to use a VQ-VAE-like architecture where the codebook is derived from the embeddings of real words in an LLM's vocabulary. This forces the model to represent the abstract CF embedding $e$ as a combination of meaningful tokens.
Steps & Procedures:

Stage 1: Vector-quantized Disentangled Representation Mapping (Section 3.2)

This stage acts as an autoencoder that reconstructs the original CF embedding $e$ after passing it through a discrete bottleneck.
1. Codebook Construction:
  - A vocabulary of meaningful words $D$ is first selected from a large LLM vocabulary (D_LLM) using a standard corpus (COCA) to filter out non-semantic subwords or symbols.
  - The pre-trained embeddings for these words are retrieved from the LLM: $C_0 = E_{LLM}(D)$ , where $C_0$ is a large matrix of high-dimensional token embeddings.
  - To improve efficiency and align the spaces, these embeddings are projected into a lower-dimensional space using a trainable linear layer $W_c$ : $C = W_c C_0$ Here, $C \in \mathbb{R}^{|\mathcal{D}| \times d}$ is the final codebook used for quantization. $C_0$ remains frozen, so the model only learns the best projection $W_c$ .
2. Encoder (Disentanglement and Projection):
  - Input: The embedding $e$ from any pre-trained CF model.
  - Multi-Projector: The embedding $e$ is projected into $n$ different subspaces to disentangle its contained information (e.g., different aspects of a user's taste). $e_i = W_i e \quad \text{for} \quad i = 1, 2, \ldots, n$
  - Transformer Encoder: The $n$ projected vectors $(e_1, \dots, e_n)$ are fed into a transformer, which models the relationships between these different aspects and outputs a sequence of refined vectors $(z_{e_1}, \dots, z_{e_n})$ .
3. Quantization (Continuous-to-Discrete Mapping):
  - Each vector $z_e$ $z_{e}$ from the transformer output is quantized using Residual Quantization (RQ). This is an iterative process. In the first step, the model finds the closest codebook vector $c_k^{(1)}$ $c_{k}^{(1)}$ to $z_e$ $z_{e}$ . In the next step, it finds the closest codebook vector to the residual $(z_e - c_k^{(1)})$ $(z_{e} - c_{k}^{(1)})$ , and so on. $r^{(h+1)} = r^{(h)} - c_k^{(h)} \quad \text{w.r.t.} \quad k = \arg\min_j \|r^{(h)} - c_j\|_2^2$
    - $r^{(1)}$ is the initial vector $z_e$ .
    - $c_j$ is a vector from the codebook $C$ .
    - The final quantized vector is the sum of the selected codewords: $z_q = \sum_{h=1}^{H} c_k^{(h)}$ .
  - The descriptors are the tokens corresponding to the codewords chosen in the first level of quantization, as they capture the most significant information.
4. Decoder:
  - The decoder takes the sequence of quantized vectors $(z_{q_1}, \dots, z_{q_n})$ , passes them through its own transformer, and then projects them back to reconstruct the original CF embedding, $e_{re}$ .
5. Mapping Loss: The training objective for this stage is $\mathcal{L}_{map}$ , which includes:
  - A reconstruction loss $\mathcal{L}_{recons}$ to ensure the decoded vector $e_{re}$ is close to the original input $e$ . $\mathcal{L}_{recons} = \|e_{re} - e\|_2^2$
  - A quantization loss $\mathcal{L}_Q$ to train the encoder and the codebook projection. It encourages the encoder's output to be close to the chosen codebook vectors and vice versa. The sg[·] operator denotes stop-gradient, which is a standard technique in VQ-VAE training. $\mathcal{L}_Q = \sum_{h=1}^{H} (\|\mathsf{sg}[r^{(h)}] - c_k^{(h)}\|_2^2 + \beta\|\mathsf{sg}[c_k^{(h)}] - r^{(h)}\|_2^2)$
Stage 2: Contrastive Learning for Semantic Representation Alignment (Section 3.3)

This stage ensures the generated descriptors are semantically consistent with the user/item's actual textual information.
1. Generate Semantic Embeddings:
  - Summary Embedding ( $h_s$ ): For each user/item, a detailed textual summary is created (e.g., using an LLM to process item descriptions or a user's interaction history). This summary is then converted into a fixed embedding $h_s$ using an LLM-based text embedding model $\mathcal{E}$ . This serves as the "ground truth" semantic anchor.
  - Descriptors Embedding ( $h_d$ ): To make the process differentiable, the descriptors are not used as text. Instead, their low-dimensional embeddings $(z_{d_1}, \dots, z_{d_n})$ from the codebook are mapped back to the original high-dimensional LLM token space using the pseudo-inverse of $W_c$ . These are then combined with a prompt and fed directly to the same embedding model $\mathcal{E}$ to produce the descriptors embedding $h_d$ .
2. Contrastive Alignment Loss:
  - A contrastive loss function is used to align $h_d$ $h_{d}$ and $h_s$ $h_{s}$ . It aims to maximize the similarity between the descriptor embedding $h_d$ $h_{d}$ of an entity and its own summary embedding $h_s$ $h_{s}$ (a positive pair), while minimizing its similarity to the summary embeddings of all other entities in the batch (negative pairs). $\mathcal{L}_{align} = - \frac{1}{|\Omega|} \sum_{v \in \Omega} \log \frac{\phi(d_v, s_v)}{\sum_{v' \in \Omega} \phi(d_v, s_{v'})}$
    - $\Omega$ is the batch of users/items.
    - $\phi(d, s)$ is the cosine similarity between the descriptor embedding $\mathbf{h}_d$ and the summary embedding $\mathbf{h}_s$ , scaled by a temperature parameter $\tau$ .
Overall Optimization: The final loss is a weighted sum of the original recommender's loss $\mathcal{L}_{\mathcal{R}}$ , the mapping loss $\mathcal{L}_{map}$ , and the alignment loss $\mathcal{L}_{align}$ . $\mathcal{L} = \mathcal{L}_{\mathcal{R}} + \mu \mathcal{L}_{map} + \lambda \mathcal{L}_{align}$ Training is done in a stable, 3-step curriculum:
1. Pre-train the base CF model.
2. Train the FACE autoencoder to learn the mapping (L_map).
3. Fine-tune the entire system with all losses activated to achieve semantic alignment.

5. Experimental Setup

Datasets:
- Amazon-book: A widely used dataset for book recommendations from Amazon.
- Yelp: A dataset containing user reviews for businesses, often used for service/local business recommendation.
- Steam: A dataset of user interactions with video games on the Steam platform.
- These datasets were chosen because they contain rich textual information (reviews, descriptions) alongside user-item interactions, which is essential for the semantic alignment step.
Evaluation Metrics:
- Recall@N:
  1. Conceptual Definition: Measures the proportion of relevant items (items the user actually interacted with in the test set) that are found within the top-N recommended items. It answers the question: "Out of all the items the user liked, how many did we manage to recommend in our top-N list?" It focuses on coverage.
  2. Mathematical Formula: $\text{Recall@N} = \frac{|\{\text{Recommended Items}\} \cap \{\text{Relevant Items}\}|}{|\{\text{Relevant Items}\}|}$
  3. Symbol Explanation:
    - {Recommended Items}: The set of top-N items recommended to a user.
    - {Relevant Items}: The set of items in the test set that the user has a positive interaction with.
- Normalized Discounted Cumulative Gain (NDCG@N):
  1. Conceptual Definition: A metric that evaluates the quality of the ranking of recommended items. It gives higher scores for putting more relevant items at the top of the list. Unlike Recall, it considers the position of the hit. It is "normalized" so that the perfect ranking has a score of 1.0, allowing for fair comparisons across different users.
  2. Mathematical Formula: $\text{NDCG@N} = \frac{\text{DCG@N}}{\text{IDCG@N}}, \quad \text{where} \quad \text{DCG@N} = \sum_{i=1}^{N} \frac{rel_i}{\log_2(i+1)}$
  3. Symbol Explanation:
    - $rel_i$ : The relevance of the item at rank $i$ . In this context, it is 1 if the item is relevant (interacted with) and 0 otherwise.
    - DCG@N: Discounted Cumulative Gain, which sums the relevance scores penalized by their rank.
    - IDCG@N: Ideal Discounted Cumulative Gain, which is the DCG score of a perfect ranking (all relevant items ranked at the top).
Baselines: The framework is tested by applying it to several existing CF models and comparing the base model vs. base model + FACE.
- GMF: A classic matrix factorization model.
- LightGCN: A state-of-the-art graph-based CF model that simplifies GNNs for recommendation.
- SimGCL & LightGCL: Advanced CF models that use contrastive learning to improve representation quality.
- RLMRec: A strong baseline that already enhances a CF model (LightGCN) by aligning its embeddings with textual information using contrastive learning. This tests if FACE can provide additional benefits over existing alignment methods.

6. Results & Analysis

Core Results: The following table, transcribed from Table 1 in the paper, shows the main performance comparison. R@N stands for Recall@N and N@N for NDCG@N. The second row for each model shows the results with FACE applied.

Manual Transcription of Table 1: Overall performance comparison

Dataset	Amazon-book				Yelp				Steam
Dataset	R@5	R@20	N@5	N@20	R@5	R@20	N@5	N@20	R@5	R@20	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	0.0659	0.0955	0.0414	0.1120	0.0483	0.0717	0.0547	0.1411	0.0594	0.0888
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
+ 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
SimGCL	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	0.0747	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	0.0816	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	0.0663	0.0981	0.0426	0.1165	0.0495	0.0737	0.0545	0.1408	0.0599	0.0887
+ 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

Analysis:

Consistent Improvement: FACE consistently improves the performance of all five backbone models across all three datasets. This confirms its effectiveness and generality as a plug-in module. For instance, with GMF on Yelp, FACE improves Recall@20 by 6.5% (from 0.1052 to 0.1120).
Effectiveness on Strong Baselines: FACE enhances even powerful contrastive models like SimGCL and LightGCL, demonstrating that its semantic alignment provides benefits beyond what structural augmentation or embedding perturbation can offer.
Synergy with Text-Aware Models: Even when applied to RLMRec, which already aligns CF with text, FACE provides further gains. This suggests that FACE's method of mapping to discrete, semantic tokens is a more effective form of alignment than simply mapping to a continuous vector space.

Interpretability Studies:
- Item Recovery (Figures 2 & 3):
  - Item-Retrieval Task: When an LLM was given the descriptors for an item and a list of candidates, it could retrieve the correct item with high accuracy (Figure 2). This shows that the descriptors contain specific, identifiable semantic information about the item.
  - Item-Generation Task: When an LLM was asked to generate a new item description based only on the descriptors, the generated description was significantly more similar to the original ("Truth") item than to other random items in the dataset (Figure 3). This confirms that LLMs can not only recognize but also creatively use the semantics captured in the descriptors.
    
    该图像是三个柱状图组成的图表，展示了不同候选数量下Amazon、Yelp和Steam数据集的商品检索准确率，随着候选数增加，准确率呈下降趋势。
    
    该图像是一个箱线图，展示了在Amazon、Yelp和Steam三个数据集上，所有项目及真实项目的相似度分布情况，比较了“All”和“Truth”两种条件下的相似度差异。
- Real User Study on Interaction Interpretation (Table 2): This study evaluated how well LLM-generated explanations for user-item interactions were perceived. Explanations were generated based on either RLMRec profiles (long paragraphs) or FACE descriptors (a few words).
  
  Manual Transcription of Table 2: Ranking Results
  
  Method Manual LLM
  
  RLMRec Profile 1.935 1.800
  
  FACE Descriptors 1.915 1.700
  
  Analysis: The ranking task required annotators to rank four explanations, one of which corresponded to a true positive interaction. A perfect method would have the true positive ranked 1st every time (average rank of 1). The lower average rank for FACE Descriptors indicates that explanations based on them were considered more reliable and convincing by both human annotators and another LLM. This is particularly impressive as FACE achieves this with just 16 tokens, compared to a full paragraph for RLMRec, demonstrating superior efficiency and semantic density.

Ablation / Parameter Sensitivity:

Hyperparameter Analysis (Figure 4):
- Descriptor Number (n): Performance improves as $n$ increases from 1 to 16, showing that disentangling the CF embedding into more aspects helps capture more information. Performance plateaus or slightly drops beyond 16, likely due to redundancy.
- Codebook Dimension (d): A dimension of 256 seems to be the sweet spot. Lower dimensions (64) are too restrictive, while higher dimensions (512) might lead to overfitting.
- $Alignment Weight (λ)$ : The alignment loss is crucial. Performance is poor with a low $λ$ and is hindered if $λ$ is too high, as the model over-prioritizes textual alignment at the expense of the core recommendation task.
  
  该图像是图表，展示了超参数对模型召回率Recall@20的敏感性分析结果，分别包括码本维度（Codebook Dimension）、描述符数量（Descriptor Number）和对齐权重（Alignment Weight），比较了LightGCN和GMF两种模型在不同设置下的表现。

Ablation Studies (Table 3): This study validates the contribution of each component of the FACE architecture.

Manual Transcription of Table 3: Ablation studies

Dataset	Variant	Recall@20	NDCG@20
Amazon-book	Full	0.1622	0.1009
	w/o trans	0.1611	0.0994
	w/o recons	0.1586	0.0981
	w/o align	0.1565	0.0962
Yelp	Full	0.1203	0.0766
	w/o trans	0.1200	0.0762
	w/o recons	0.1191	0.0760
	w/o align	0.1171	0.0741

Analysis:

w/o align: Removing the contrastive alignment loss causes the most significant performance drop. This proves that forcing the descriptors to be semantically meaningful is the key driver of the performance boost.
w/o recons: Removing the reconstruction loss also hurts performance. This loss ensures that the descriptors collectively retain enough information to reconstruct the original CF embedding, preventing information loss.
w/o trans: Removing the transformer modules has a smaller but still negative impact. This indicates that modeling the inter-dependencies between the disentangled aspects is beneficial.

7. Conclusion & Reflections

Conclusion Summary: The paper successfully introduces FACE, a novel and general framework that bridges the gap between CF models and LLMs. By mapping abstract CF embeddings to discrete, interpretable LLM tokens (descriptors), FACE enables frozen LLMs to understand user preferences from collaborative filtering. This is achieved via a sophisticated autoencoder with a quantized codebook and is refined through a contrastive alignment loss. The framework not only boosts the performance of various CF models but also significantly enhances the interpretability of their recommendations, as validated by extensive experiments.
Limitations & Future Work:
- Training Complexity: The 3-step training curriculum, while effective, is more complex to implement and manage than a single end-to-end training process.
- Computational Cost: The framework relies on multiple LLM calls during training (for generating summaries and for the alignment loss), which can be computationally expensive and may pose scalability challenges for very large datasets.
- Dependence on Textual Data: The alignment stage depends on the availability of high-quality textual data (summaries, reviews, etc.) to create the semantic anchors. Its effectiveness might be reduced in domains where such text is scarce or noisy.
- Future work mentioned by the authors includes leveraging the interpretable descriptors to support more complex downstream recommendation tasks, such as conversational recommendation or fine-grained explanation generation.
Personal Insights & Critique:
- Novelty and Impact: The core innovation of mapping to discrete, semantic tokens instead of just aligning continuous spaces is a significant conceptual leap. It moves from "making the numbers look similar" to "translating the numbers into a shared language." This is a more direct and powerful way to achieve synergy between numeric (CF) and symbolic (LLM) AI systems.
- Transferability: The model-agnostic nature of FACE is a major strength. It could potentially be applied to other domains where abstract embeddings need to be made interpretable, such as in computer vision (interpreting image embeddings) or bioinformatics (interpreting protein embeddings).
- Untested Assumptions: The quality of the descriptors is heavily dependent on the quality of the initial LLM vocabulary filtering and the "summary" generation for alignment. Biases in the LLM used for generating summaries could propagate into the learned descriptors.
- Open Questions: How would FACE perform in a completely cold-start scenario where no textual summary can be generated for a new user or item? While the decoder can act as a generator from keywords, its practical effectiveness in bootstrapping new entities is an interesting area for future exploration. Furthermore, the scalability of this approach to industrial-scale recommender systems with billions of items remains a critical open question.

Method	Manual	LLM
RLMRec Profile	1.935	1.800
FACE Descriptors	1.915	1.700