Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models

TL;DR

This paper introduces a novel LLM-based method to infer user interests and enhance knowledge-aware recommendations by constructing a hybrid interest knowledge graph, significantly improving performance for users with sparse data.

Contribution

The paper presents a new LLM-driven approach to generate user-side knowledge, integrating it into a hybrid graph for improved recommendations, especially for sparse user data scenarios.

Findings

Achieves state-of-the-art recommendation performance on three datasets.

Effectively mitigates noise through user interest reconstruction and contrastive learning.

Improves recommendations for users with limited interaction history.

Abstract

In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenges, including the risk of noisy information and the need for additional knowledge transfer. In this paper, we propose an LLM-based user-side knowledge inference method alongside a carefully designed recommendation framework to address these challenges. Our approach employs LLMs to infer user interests based on historical behaviors, integrating this user-side information with item-side and collaborative data to construct a hybrid structure: the Collaborative Interest Knowledge Graph (CIKG). Furthermore, we propose a CIKG-based recommendation framework that includes a user interest reconstruction module and a cross-domain contrastive learning module to mitigate potential noise and facilitate knowledge transfer. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our method. Our approach achieves state-of-the-art performance compared to competitive baselines, particularly for users with sparse interactions.