Justified recommendations based on content and rating data
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Providing justification to a recommendation gives credibility to a recommender system. Some recommender systems (Amazon.com etc.) try to explain their recommendations, in an effort to regain customer acceptance and trust. But their explanations are poor and unjustified, because they are based solely on rating or navigational data, ignoring the content data. In this paper, we propose a novel approach that attains simultaneously accurate and justifiable recommendations. We construct a feature profile for the users, to reveal their favorite features. Moreover, we create biclusters (i.e. group of users which exhibit highly correlated ratings on groups of items) to exploit partial matching between the preferences of the test user and each community of users. We have evaluated the quality of our justifications with an objective metric in a real data set, showing the superiority of the proposed approach. We also conducted a user study to measure users ’ satisfaction against the existing and our proposed justification style. The user study shows that our justification style is users ’ favorite choice.