Recommender systems sometimes fail in recommending the right content to users having eclectic tastes; they can, especially during the first interactions, incur in over-specialization and popularity bias problems. We are investigating on how this problem can be handled and in particular on how to effectively induce novelty and serendipity in recommendations in order to increase user satisfaction.
Handling eclectic tastes in recommender systems : novelty, serendipity and mentors / E. Tacchini. ((Intervento presentato al 4. convegno Multimedia, Distributed and Pervasive Systems Workshop tenutosi a Passau nel 2010.
Handling eclectic tastes in recommender systems : novelty, serendipity and mentors
E. TacchiniPrimo
2010
Abstract
Recommender systems sometimes fail in recommending the right content to users having eclectic tastes; they can, especially during the first interactions, incur in over-specialization and popularity bias problems. We are investigating on how this problem can be handled and in particular on how to effectively induce novelty and serendipity in recommendations in order to increase user satisfaction.
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