用大模型增强扩散模型，更好理解用户意图推荐长尾商品
LLMDiRec: LLM-Enhanced Intent Diffusion for Sequential Recommendation
- 将大模型语义表示与用户行为编码动态融合
- 在五个数据集上超越现有方法，尤其提升长尾物品推荐效果
- 适合关注用户意图建模与冷启动问题的研究者
现有序列推荐模型，即使先进的基于扩散的方法，也难以捕捉用户行为背后的丰富语义意图，尤其对新用户或长尾物品表现不佳。这源于其依赖缺乏语义基础的ID嵌入。我们提出LLMDiRec，通过将大语言模型（LLMs）融入意图感知的扩散模型来弥补这一差距。该方法结合ID嵌入的协同信号与LLM提供的丰富语义表征，采用动态融合机制和多任务目标对齐两种视图。我们在五个公开数据集上进行了广泛实验，结果表明， modelname 在捕捉复杂用户意图及提升长尾物品推荐性能方面均显著优于现有最先进算法。
原文摘要 · Abstract (English)
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.
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