A survey on effects of adding explanations to recommender systems. (19th January 2022)

• Record Type: Journal Article
• Authors: Vultureanu‐Albişi, Alexandra; Bădică, Costin
• Other Names: Wright Steven A. guestEditor.; Solak Serdar guestEditor.; Kilimci Zeynep Hilal guestEditor.; Eken Süleyman guestEditor.; Fernandes Steven guestEditor.; Zhang Yu‐Dong guestEditor.; Tavares João Manuel R.S. guestEditor.
• Is Part Of: Concurrency and computation. Volume 34:Number 20(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-19
• Subjects: explainable artificial intelligence -- human‐centered evaluation -- intelligent human‐interaction‐computer -- recommender systems; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6834
• Languages: English
• ISSNs: 1532-0626

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.