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arXiv 1905.01986cs.IRcs.AI

Beyond Personalization: Research Directions in Multistakeholder Recommendation

University of Colorado, Boulder(科罗拉多大学博尔德分校);University of Minnesota(明尼苏达大学);eBay, Inc.(eBay公司);University of Klagenfurt(克拉根福大学);National Institute of Advanced Industrial Science and Technology(日本产业技术综合研究所);Expedia Group(Expedia集团);Accenture(埃森哲)

Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Pizzato

英文摘要:

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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