从被忽视到被探索：基于视角混合的物品关系恢复用于序列推荐
From Overlooked to Explored: Recovering Item Relations via Mixture of Perspectives for Sequential Recommendation
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中文总结 AI 辅助
针对序列推荐中Transformer模型的相似性偏差问题，提出PRISM模块，通过多视角透镜校准注意力以捕捉物品全谱关系，在七个基准上优于现有模型。
中文摘要 AI 辅助
从用户的交互序列中捕捉用户偏好是序列推荐（Sequential Recommendation, SR）的核心挑战。这种偏好直观地源自物品间的关系：每个物品转移都反映了嵌入在物品关系中的偏好，因此忠实地捕捉这些关系对准确推荐至关重要。正因如此，自注意力机制在序列推荐中占据主导地位，因为它具备计算物品间两两交互的能力，但我们的实证分析显示，各类基于Transformer的SR模型始终存在相似性偏差：点积注意力分数会不成比例地偏向相似物品，系统性地忽视了包含有意义偏好信号的异质关系，直接限制了推荐性能。为解决这一问题，我们提出PRISM（Perspective-based Relational Insight Synthesis Module，基于视角的关系洞察合成模块），该模块从多个视角重新审视物品关系。PRISM采用K个视角透镜（Perspective Lenses）从不同视角校准注意力，结合优化同质关系的亲和视图（Affinity View）和揭示被相似性偏差抑制的异质关系的对比视图（Contrast View），使模型能够捕捉用户偏好的全谱。在七个真实世界基准上的大量实验表明，PRISM始终优于当前最先进的基线模型。我们的代码可在该https URL获取。
英文摘要
Capturing user preference from a user's interaction sequence is the central challenge of Sequential Recommendation (SR). This preference intuitively emerges from inter-item relations: each item transition reflects a preference embedded in the relations between items, making the faithful capture of these relations essential for accurate recommendation. For this reason, self-attention is dominant in sequential recommendation for its ability to compute pairwise item interactions, yet our empirical analysis reveals that it consistently suffers from similarity bias across various types of transformer-based SR models: dot-product attention scores disproportionately favor similar items, systematically overlooking heterogeneous relations with meaningful preference signals and directly limiting recommendation performance. To address this, we propose PRISM (Perspective-based Relational Insight Synthesis Module), a module that re-examines item relations from multiple perspectives. PRISM employs K Perspective Lenses to calibrate attention from distinct viewpoints, combining an Affinity View that refines homogeneous relations and a Contrast View that exposes heterogeneous ones suppressed by similarity bias, enabling the model to capture the full spectrum of user preferences. Extensive experiments on seven real-world benchmarks demonstrate that PRISM consistently outperforms state-of-the-art baselines. Our code is available at https://github.com/327aem/PRISM/.
发表机构
- Pohang University of Science and Technology（浦项科技大学）
- Sungkyunkwan University（成均馆大学）
机构由 AI 辅助整理，请以论文原文为准。
