6533b85afe1ef96bd12b9712
RESEARCH PRODUCT
A Serendipity-Oriented Greedy Algorithm for Recommendations
Denis KotkovJari VeijalainenShuaiqiang Wangsubject
ta113SerendipityComputer sciencebusiness.industrysuosittelujärjestelmät020207 software engineeringserendipity02 engineering and technologyalgorithmsunexpectednessnoveltyalgoritmit0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencerecommender systemsGreedy algorithmbusinessGreedy randomized adaptive search proceduredescription
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 feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most common metric. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 |