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

Minjie Hong, Yan Xia, Zehan Wang, Jieming Zhu, Ye Wang, Sihang Cai, Xiaoda Yang, Quanyu Dai, Zhenhua Dong, Zhimeng Zhang, Zhou Zhao

Track: User modeling, personalization and recommendation

Keywords: Recommender systems, large language models (LLM), Behavior-Semantic Collaboration

TL;DR: LLM-BS is a decoder-only LLM-based generative recommendation framework that integrates endogenous and exogenous Behavioral and Semantic signal in a non-intrusive manner.

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.

Submission Number: 890