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Providing justification to a recommendation gives credibility to a recommender system. Some recommender systems 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, the authors propose a novel approach that attains simultaneously accurate and justifiable recommendations. They construct a feature profile for the users, to reveal their favorite features. Moreover, they 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.
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