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
© 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.
PublisherSCITEPRESS Science And Technology Publications
Parent publication ISBN978-989-758-246-2
ConferenceInternational conference on web information systems and technologies
Is part of publicationWEBIST 2017 : Proceedings of the 13rd International conference on web information systems and technologies. Volume 1
Publication in research information system
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How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm Kotkov, Denis; Veijalainen, Jari; Wang, Shuaiqiang (Springer Wien, 2020)Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, ...
Kotkov, Denis (University of Jyväskylä, 2018)The number of goods and services (such as accommodation or music streaming) offered by e-commerce websites does not allow users to examine all the available options in a reasonable amount of time. Recommender systems are ...
Kotkov, Denis; Veijalainen, Jari; Wang, Shuaiqiang (SCITEPRESS, 2016)Most recommender systems suggest items similar to a user profile, which results in boring recommendations limited by user preferences indicated in the system. To overcome this problem, recommender systems should suggest ...
Pandey, Gaurav; Kotkov, Denis; Semenov, Alexander (ACM Press, 2018)Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, ...
Kotkov, Denis; Wang, Shuaiqiang; Veijalainen, Jari (Springer International Publishing AG, 2017)Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works ...