LLM-BS: Enhancing Large Language Models for Recommendation through Exogenous Behavior-Semantics Integration

Abstract

Large language models (LLMs) are increasingly leveraged as foundational backbones in the development of advanced recommender systems, offering enhanced capabilities through their extensive knowledge and reasoning. Existing LLM-based recommender systems (RSs) often face challenges due to the significant differences between the linguistic semantics of pre-trained LLMs and the collaborative semantics essential for RSs. Typically, these systems apply pre-trained linguistic semantics while learning collaborative semantics from scratch using the LLM-Backbone. However, as LLM architectures are not inherently tailored for recommendation tasks, this approach results in inefficient learning of collaborative information, poor understanding of result correlations, and a failure to leverage traditional RSs features effectively. To address these challenges, we propose LLM-BS, a decoder-only LLM-based generative recommendation framework that integrates endogenous and exogenous Behavioral and Semantic information in a non-intrusive manner. Specifically, we propose 1) a dual-source, knowledge-rich item indexing scheme that integrates indexing sequences for exogenous signals, enabling efficient link-wide processing; 2) a multi-scale reconfiguration alignment that non-intrusively guides the model toward a deeper understanding of both collaborative and semantic signals; 3) an Annealing Adapter designed to finely balance the model’s recommendation performance with its comprehension capabilities. We demonstrate LLM-BS’s effectiveness through rigorous testing on three public benchmarks.