Beyond Personalization: Research Directions in Multistakeholder Recommendation
This work identifies a gap in academic research for recommender systems that affect multiple stakeholders beyond users, such as providers and platforms, but it is incremental as it reviews and synthesizes existing concepts rather than introducing new methods.
The paper addresses the limitation of focusing solely on user personalization in recommender systems, proposing multistakeholder recommendation as a framework to incorporate fairness, balance, profitability, and reciprocity, and outlines research directions and examples in this emerging field.
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.