发表机构
University of Colorado Boulder; Tel Aviv University（科罗拉多大学博尔德分校; 特拉维夫大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对推荐系统解释多聚焦单个项目的问题，提出挖掘用户资料的成对反事实解释任务，基于反事实学习挖掘促成项目相对排名的用户资料项目，为比较性解释提供依据。
AI 中文摘要
推荐系统的解释主题自该领域早期起就受到持续的研究关注。除少数例外，相关工作主要聚焦于推荐列表中单个项目的解释，且尤其近年来，研究强调与推荐算法本身逻辑解耦的方法。基于人际沟通心理学的发现，本文提出一项新任务：项目排名的成对解释，即提出比较性问题“为何项目A的排名高于项目B？”。本文认为，该任务的有效解决方案本质上需基于推荐算法的运行机制。本文提出一类基于反事实学习的技术，用于挖掘用户资料中促成项目相对排名的项目。通过多个数据集，本文证明能够识别此类项目，作为比较性解释的潜在依据。
英文摘要
The topic of explanation in recommender systems has seen steady research attention since the earliest days of the field. With some exceptions, this work has focused on the explanation of single items in a recommendation list and, especially recently, has emphasized approaches that are decoupled from the logic of the recommendation algorithm itself. Based on findings in the psychology of interpersonal communication, we propose a new task, pairwise interpretation of item rankings, asking the comparative question ``Why is item A ranked higher than item B?''. An effective solution to this task, we argue, is inherently grounded in the operation of the recommendation algorithm. We propose a class of techniques based on counterfactual learning to uncover the items in a user's profile that have contributed to the relative ranking of items. Using multiple datasets, we show that it is possible to identify such items as potential basis for comparative explanation.
