aiqbench analysis
This research advances explainable AI by introducing a counterfactual learning framework specifically designed to interpret pairwise item rankings in recommender systems. By grounding explanations directly within the recommendation algorithm's logic, it addresses the growing need for transparent comparative reasoning rather than isolated item justifications. The work contributes significantly to the field of interpretable machine learning models used in large-scale content delivery.
Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals
arXiv:2608.21662v1 Announce Type: cross Abstract: 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.
Title:Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals
Abstract: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.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
arXiv:2608.21662 [cs.IR]
(or arXiv:2608.21662v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2608.21662 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 256 words
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