Artificial Intelligence Review

, Volume 24, Issue 2, pp 179-197

First online:

Explanation in Recommender Systems

  • David McsherryAffiliated withSchool of Computing and Information Engineering, University of Ulster Email author 

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There is increasing awareness in recommender systems research of the need to make the recommendation process more transparent to users. Following a brief review of existing approaches to explanation in recommender systems, we focus in this paper on a case-based reasoning (CBR) approach to product recommendation that offers important benefits in terms of the ease with which the recommendation process can be explained and the system’s recommendations can be justified. For example, recommendations based on incomplete queries can be justified on the grounds that the user’s preferences with respect to attributes not mentioned in her query cannot affect the outcome. We also show how the relevance of any question the user is asked can be explained in terms of its ability to discriminate between competing cases, thus giving users a unique insight into the recommendation process.


attribute-selection strategy case-based reasoning explanation recommender systems