通过反事实学习，解释为何推荐A比B更优
Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals
- 基于反事实学习，挖掘用户画像中影响排名差异的关键项目
- 在多个数据集上验证可定位导致排序差异的用户偏好项
- 适合需要可解释推荐逻辑的研究者和产品设计者
推荐系统解释研究长期聚焦于单个推荐项的原因说明，尤其近期多采用与推荐算法解耦的方法。受人际沟通心理学启发，本文提出新任务：对推荐列表中两项的相对排序进行成对解释，即回答“为何A排在B之前？”。我们认为，有效解决该问题需根植于推荐算法本身运作逻辑。为此，提出基于反事实学习的一类技术，用于识别用户画像中影响项目相对排序的关键因素。在多个数据集上的实验表明，可成功定位这些潜在解释依据。
原文摘要 · Abstract (English)
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.
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