标题： FACE：将协同过滤嵌入映射到LLM标记的通用框架 显示英文标题

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

摘要： 近年来，大型语言模型（LLMs）已被探索用于与基于协同过滤（CF）的推荐系统集成，这对于个性化用户体验至关重要。 然而，一个关键挑战是 LLMs难以解释CF方法产生的潜在的非语义嵌入，这限制了推荐效果和进一步应用。 为了解决这个问题，我们提出了FACE，一个通用的可解释框架，将CF嵌入映射到预训练的LLM标记。 具体而言，我们引入了一个解耦投影模块，将CF嵌入分解为特定概念的向量，然后设计了一个量化自编码器，将连续嵌入转换为LLM标记（描述符）。 随后，我们设计了一个对比对齐目标，以确保标记与相应的文本信号对齐。 因此，模型无关的FACE框架在不微调LLMs的情况下实现了语义对齐，并通过利用其预训练能力提高了推荐性能。 在三个真实世界推荐数据集上的实证结果表明，基准模型的性能得到了提升，可解释性研究证实了描述符的可解释性。 代码可在https://github.com/YixinRoll/FACE中获得。 显示英文摘要

摘要： 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.