为推荐系统提供用户群体整体推荐理由的简洁解释。
Path-based summary explanations for graph recommenders (extended version)
- 用斯坦纳树算法压缩路径解释，降低复杂度
- 总结多用户多物品推荐原因，揭示集体行为模式
- 适合模型开发者理解推荐逻辑，提升可解释性
基于路径的解释能深入揭示图推荐模型的内在机制。然而，以往研究主要聚焦于单个用户对单个物品的推荐解释。本文提出总结性解释——通过突出一群用户为何被推荐一组物品，或一个物品为何被推荐给一群用户，来揭示推荐系统的集体行为特征。我们设计了一种新方法，利用高效的图算法（如斯坦纳树与带奖励的斯坦纳树）生成解释摘要，在保持关键信息的同时显著减少解释的规模与复杂度，使解释更易理解且对模型开发者更有价值。在多个评估指标下，我们的方法在多数场景中优于现有基线方法，展现出更优的解释质量。
原文摘要 · Abstract (English)
Path-based explanations provide intrinsic insights into graph-based recommendation models. However, most previous work has focused on explaining an individual recommendation of an item to a user. In this paper, we propose summary explanations, i.e., explanations that highlight why a user or a group of users receive a set of item recommendations and why an item, or a group of items, is recommended to a set of users as an effective means to provide insights into the collective behavior of the recommender. We also present a novel method to summarize explanations using efficient graph algorithms, specifically the Steiner Tree and the Prize-Collecting Steiner Tree. Our approach reduces the size and complexity of summary explanations while preserving essential information, making explanations more comprehensible for users and more useful to model developers. Evaluations across multiple metrics demonstrate that our summaries outperform baseline explanation methods in most scenarios, in a variety of quality aspects.
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