Exploiting reviews to guide users' selections

Abstract

We introduce HRS, a recommender that exploits user reviews and identifies the features that are most likely appealing to users. HRS incorporates this knowledge into the recommendation process to generate a list of top-k recommendations, each of which is paired with an explanation that (i) showcases why a particular item was recommended and (ii) helps users decide which items, among the ones recommended, are best tailored towards their individual interests. Empirical studies conducted using the Amazon dataset demonstrate the correctness of the proposed methodology.