2019/05/31 by Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke +5 · 259 citations
Computer Science · #cs.AI #cs.IR
paper · pdf · doi:10.1007/s11257-019-09256-1
published in User Modeling and User-Adapted Interaction 30(1), 127-158 (Springer Science and Business Media LLC) · 64 pages
arxiv created 2019/12/15 · crossref issued 2020/01/10 · crossref published 2020/01/10 · crossref published-online 2020/01/10 · crossref created 2020/01/10 · crossref published-print 2020/03/01 · arxiv updated 2020/07/16 · crossref deposited 2021/01/09 · crossref indexed 2026/08/07
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.