FACE: A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens

• URL: http://arxiv.org/abs/2510.15729v1
• Date: Fri, 17 Oct 2025 15:19:54 GMT
• 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, Yanyong Zhang,
• Abstract summary: Large language models (LLMs) have been explored for integration with collaborative filtering (CF)-based recommendation systems.<n>A key challenge is that LLMs struggle to interpret the latent, non-semantic embeddings produced by CF approaches.<n>We propose FACE, a general interpretable framework that maps CF embeddings into pre-trained LLM tokens.
• Score: 28.971310672971914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/YixinRoll/FACE.

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