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Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through featuredoi:10.5220/0006232800320040 dblp:conf/webist/KotkovVW17 fatcat:j2mau6f2ivcpjnnffavjd5jr5u