LLMDiRec: LLM-Enhanced Intent Diffusion for Sequential Recommendation

TL;DR

LLMDiRec enhances sequential recommendation by integrating LLM-derived semantic intent with traditional ID-based methods, significantly improving performance especially for long-tail items and complex user behaviors.

Contribution

This paper introduces LLMDiRec, a novel model that fuses LLM-based semantic intent with collaborative signals for improved sequential recommendation.

Findings

Outperforms state-of-the-art algorithms on five datasets.

Significantly improves recommendation accuracy for long-tail items.

Enhances modeling of complex user intents.

Abstract

Existing sequential recommendation models, even advanced diffusion-based approaches, often struggle to capture the rich semantic intent underlying user behavior, especially for new users or long-tail items. This limitation stems from their reliance on ID-based embeddings, which lack semantic grounding. We introduce LLMDiRec, a new approach that addresses this gap by integrating Large Language Models (LLMs) into an intent-aware diffusion model. Our approach combines collaborative signals from ID embeddings with rich semantic representations from LLMs, using a dynamic fusion mechanism and a multi-task objective to align both views. We run extensive experiments on five public datasets. We run extensive experiments on five public datasets. We demonstrate that \modelname outperforms state-of-the-art algorithms, with particularly strong improvements in capturing complex user intents and enhancing recommendation performance for long-tail items.