{"ID":2852648,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.03259","arxiv_id":"2601.03259","title":"LLMDiRec: LLM-Enhanced Intent Diffusion for Sequential Recommendation","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.","short_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 LLM...","url_abs":"https://arxiv.org/abs/2601.03259","url_pdf":"https://arxiv.org/pdf/2601.03259v1","authors":"[\"Bo-Chian Chen\",\"Manel Slokom\"]","published":"2025-10-20T08:55:26Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Diffusion Model\",\"Large Language Model\",\"Language Model\"]","has_code":false}