MV-LLMRec: Multi-View Representation Learning with Large Language Models for Recommendation (Student Abstract)

摘要

Traditional recommenders often fail to disentangle the motivations behind user choices. To address this, we propose MV-LLMRec, a framework that models interactions through three views: Structural, Intent, and Conformity. MV-LLMRec leverages LLMs to generate rich semantic representations for intent and conformity, which are refined through graph propagation and dynamically fused via an attention mechanism. We evaluate MV-LLMRec on the Amazon-Movie and Amazon-Book datasets and show that it significantly outperforms state-of-the-art baselines, validating our approach.