analiza aiqbench
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)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 256 cuvinte
Subiecte conexe
RareSense: Rarity-Aware Similarity Search for Anomaly Retrieval in Transactional Data
arXiv · 3 aug. 2026
Game Design Creativity evals
LessWrong · 12 oct. 2026
Beyond Task-Only Matching: Personalized Skill Routing with Counterfactual Evaluation
arXiv · 1 sept. 2026
State of the Field: AI for Epistemics and Coordination
LessWrong · 2 oct. 2026