Economic Recommender Systems -- A Systematic Review

TL;DR

This paper systematically reviews economic recommender systems, highlighting how they incorporate monetary factors to directly optimize organizational financial goals, beyond traditional user-centric recommendations.

Contribution

It provides a comprehensive categorization and analysis of existing literature on economic recommender systems, including technical approaches, evaluation methods, and future research directions.

Findings

Identified 133 relevant papers on economic recommender systems.

Classified approaches based on technical and methodological dimensions.

Discussed limitations and future research directions in the field.

Abstract

Many of today's online services provide personalized recommendations to their users. Such recommendations are typically designed to serve certain user needs, e.g., to quickly find relevant content in situations of information overload. Correspondingly, the academic literature in the field largely focuses on the value of recommender systems for the end user. In this context, one underlying assumption is that the improved service that is achieved through the recommendations will in turn positively impact the organization's goals, e.g., in the form of higher customer retention or loyalty. However, in reality, recommender systems can be used to target organizational economic goals more directly by incorporating monetary considerations such as price awareness and profitability aspects into the underlying recommendation models. In this work, we survey the existing literature on what we call Economic Recommender Systems based on a systematic review approach that helped us identify 133 relevant papers. We first categorize existing works along different dimensions and then review the most important technical approaches from the literature. Furthermore, we discuss common methodologies to evaluate such systems and finally outline the limitations of today's research and future directions.