A Survey of Explanations in Recommender Systems

TLDR

The paper reviews explanations in recommender systems, examines how recommendation presentation influences explanations, and explores different interaction methods with explanations. It surveys existing explanation techniques, outlines seven benefits, discusses evaluation metrics, and reviews interaction approaches. The study shows that presentation style affects explanation effectiveness and illustrates these effects with examples from current applications.

Abstract

This paper provides a comprehensive review of explanations in recommender systems. We highlight seven possible advantages of an explanation facility, and describe how existing measures can be used to evaluate the quality of explanations. Since explanations are not independent of the recommendation process, we consider how the ways recommendations are presented may affect explanations. Next, we look at different ways of interacting with explanations. The paper is illustrated with examples of explanations throughout, where possible from existing applications.