用大模型语义信息修复推荐序列噪声，提升冷启动表现
Empowering Denoising Sequential Recommendation with Large Language Model Embeddings
- 融合协同过滤与大模型语义嵌入，双模态对齐识别噪声
- 在四个数据集上显著提升推荐精度，冷启动场景效果更优
- 适合需要处理噪声交互、提升长短期兴趣建模的推荐系统
序列推荐旨在通过建模用户-物品交互序列捕捉用户偏好。然而，这类模型常受意外点击等噪声干扰，导致性能下降。现有方法通过显式识别并移除噪声项来缓解问题，但仅依赖协同信号可能导致过度去噪，尤其在冷启动物品上表现不佳。为此，本文提出兴趣对齐去噪框架（IADSR），融合协同与语义信息。第一阶段分别从传统序列推荐模型和大语言模型获取物品的协同嵌入与语义嵌入；第二阶段对齐两类嵌入，并基于协同与语义模态中捕捉的长期与短期兴趣识别交互序列中的噪声。在四个公开数据集上的实验验证了该框架的有效性及其对多种序列推荐系统的兼容性。
原文摘要 · Abstract (English)
Sequential recommendation aims to capture user preferences by modeling sequential patterns in user-item interactions. However, these models are often influenced by noise such as accidental interactions, leading to suboptimal performance. Therefore, to reduce the effect of noise, some works propose explicitly identifying and removing noisy items. However, we find that simply relying on collaborative information may result in an over-denoising problem, especially for cold items. To overcome these limitations, we propose a novel framework: Interest Alignment for Denoising Sequential Recommendation (IADSR) which integrates both collaborative and semantic information. Specifically, IADSR is comprised of two stages: in the first stage, we obtain the collaborative and semantic embeddings of each item from a traditional sequential recommendation model and an LLM, respectively. In the second stage, we align the collaborative and semantic embeddings and then identify noise in the interaction sequence based on long-term and short-term interests captured in the collaborative and semantic modalities. Our extensive experiments on four public datasets validate the effectiveness of the proposed framework and its compatibility with different sequential recommendation systems.
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