A survey on effects of adding explanations to recommender systems

Abstract

Abstract Explainable recommendations become essential when we need to improve the performance of recommendations and to increase user confidence. Explanations are effective when end users can build a complete and correct mental representation of the inferential process of a recommender system. This paper presents our view on the background regarding the implications of explainability applied to recommender systems. Our work contributes to the better understanding of the concept of explainable recommendation and it offers a broader picture of the development of further research in this field. Additionally, we contribute by providing a better understanding of the concept of human‐centered evaluation of explainable recommender systems.

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