Enhancing Sequential Recommendation with World Knowledge from Large Language Models

TL;DR

This paper introduces GRASP, a novel framework that leverages large language models' world knowledge through generation and retrieval techniques, combined with holistic attention, to improve sequential recommendation accuracy despite LLM hallucinations.

Contribution

The paper proposes GRASP, a flexible framework integrating generation augmented retrieval and holistic attention to enhance sequential recommendation using LLMs' world knowledge.

Findings

GRASP achieves state-of-the-art performance on multiple datasets.

The framework effectively mitigates noise from LLM hallucinations.

It demonstrates robustness across diverse backbone models.

Abstract

Sequential Recommendation System~(SRS) has become pivotal in modern society, which predicts subsequent actions based on the user's historical behavior. However, traditional collaborative filtering-based sequential recommendation models often lead to suboptimal performance due to the limited information of their collaborative signals. With the rapid development of LLMs, an increasing number of works have incorporated LLMs' world knowledge into sequential recommendation. Although they achieve considerable gains, these approaches typically assume the correctness of LLM-generated results and remain susceptible to noise induced by LLM hallucinations. To overcome these limitations, we propose GRASP (Generation Augmented Retrieval with Holistic Attention for Sequential Prediction), a flexible framework that integrates generation augmented retrieval for descriptive synthesis and similarity retrieval, and holistic attention enhancement which employs multi-level attention to effectively employ LLM's world knowledge even with hallucinations and better capture users' dynamic interests. The retrieved similar users/items serve as auxiliary contextual information for the later holistic attention enhancement module, effectively mitigating the noisy guidance of supervision-based methods. Comprehensive evaluations on two public benchmarks and one industrial dataset reveal that GRASP consistently achieves state-of-the-art performance when integrated with diverse backbones. The code is available at: https://anonymous.4open.science/r/GRASP-SRS.