Making Use of Reviews for Good Explainable Recommendation

Abstract

Reviews are used in generating explainable recommendation. However, the use of reviews has so far not been adequately addressed. In this paper, we examine methods that make use of reviews effectively. There is a trade-off between the number and quality of reviews to use, that is, we should like to use reviews as many as possible to generate explainable recommendation, however in a large number of reviews there can be low quality ones, which can cause low quality explainable recommendation generation. We discuss new methods that use not only reviews written by a user but also those utilized by the user to generate good explainable recommendation. Our methods can be applied to different explainable recommender approaches, which is shown by adopting two state-of-the-art explainable recommender approaches in this paper. Experimental results demonstrate that our methods can be of benefit to existing explainable recommender approaches as regards both recommendation and its explanation qualities.