Multi-view Intent Learning and Alignment with Large Language Models for Session-based Recommendation

TL;DR

This paper introduces LLM4SBR, a novel two-stage framework that combines large language models and traditional session-based recommendation techniques to improve user intent understanding and recommendation accuracy while reducing training costs.

Contribution

The paper presents a lightweight, multi-view LLM-enhanced SBR framework that effectively integrates semantic and behavioral signals for session-based recommendations.

Findings

Improved recommendation performance on real datasets.

Effective semantic intent localization with LLMs.

Successful merging of semantic and behavioral representations.

Abstract

Session-based recommendation (SBR) methods often rely on user behavior data, which can struggle with the sparsity of session data, limiting performance. Researchers have identified that beyond behavioral signals, rich semantic information in item descriptions is crucial for capturing hidden user intent. While large language models (LLMs) offer new ways to leverage this semantic data, the challenges of session anonymity, short-sequence nature, and high LLM training costs have hindered the development of a lightweight, efficient LLM framework for SBR. To address the above challenges, we propose an LLM-enhanced SBR framework that integrates semantic and behavioral signals from multiple views. This two-stage framework leverages the strengths of both LLMs and traditional SBR models while minimizing training costs. In the first stage, we use multi-view prompts to infer latent user intentions at the session semantic level, supported by an intent localization module to alleviate LLM hallucinations. In the second stage, we align and unify these semantic inferences with behavioral representations, effectively merging insights from both large and small models. Extensive experiments on two real datasets demonstrate that the LLM4SBR framework can effectively improve model performance. We release our codes along with the baselines at https://github.com/tsinghua-fib-lab/LLM4SBR.