Explaining recommendations: Design and evaluation

Abstract

This chapter gives an overview of the area of explanations in recommender systems. We approach the literature from the angle of evaluation: that is, we are interested in what makes an explanation “good”. The chapter starts by describing how explanations can be affected by how recommendations are presented, and the role the interaction with the recommender system plays w.r.t. explanations. Next, we introduce a number of explanation styles, and how they are related to the underlying algorithms. We identify seven benefits that explanations may contribute to a recommender system, and relate them to criteria used in evaluations of explanations in existing recommender systems. We conclude the chapter with outstanding research questions and future work, including current recommender systems topics such as social recommendations and serendipity. Examples of explanations in existing systems are mentioned throughout.