Abstract
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.
Abstract (translated)
基于路径的解释为图推荐模型提供了内在的洞察。然而，大多数先前的研究集中于解释对某个用户单独推荐某项物品的原因。本文中，我们提出了汇总解释的概念，即强调为什么一个用户或一组用户会收到一系列物品推荐，以及为什么一项或一组物品会被推荐给一组用户的解释，作为一种有效的方法来揭示推荐系统的集体行为。此外，我们也提出了一种使用高效图算法（特别是Steiner树和带奖赏收集的Steiner树）汇总解释的新方法。我们的方法减少了摘要解释的大小和复杂性，同时保留了关键信息，使得解释对用户更加易懂，也更有利于模型开发者理解。在多个评估指标下的结果显示，在大多数场景下，我们的总结方式在各方面质量上都优于基线解释方法。
URL
https://arxiv.org/abs/2410.22020
