A Serendipity-Oriented Greedy Algorithm for Recommendations
Kotkov, D., Veijalainen, J., & Wang, S. (2017). A Serendipity-Oriented Greedy Algorithm for Recommendations. In T. A. Majchrzak, P. Traverso, K.-H. Krempels, & V. Monfort (Eds.), WEBIST 2017 : Proceedings of the 13rd International conference on web information systems and technologies. Volume 1 (pp. 32-40). SCITEPRESS Science And Technology Publications. https://doi.org/10.5220/0006232800320040
Päivämäärä
2017Tekijänoikeudet
© 2017 by SCITEPRESS – Science and Technology Publications, Lda. This is a final draft version of an article whose final and definitive form has been published by SCITEPRESS. Published in this repository with the kind permission of the publisher.
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.
Julkaisija
SCITEPRESS Science And Technology PublicationsEmojulkaisun ISBN
978-989-758-246-2Konferenssi
International conference on web information systems and technologiesKuuluu julkaisuun
WEBIST 2017 : Proceedings of the 13rd International conference on web information systems and technologies. Volume 1Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/27065316