Challenges of Serendipity in Recommender Systems
Kotkov, D., Veijalainen, J., & Wang, S. (2016). Challenges of Serendipity in Recommender Systems. In T. A. Majchrzak, P. Traverso, V. Monfort, & K.-H. Krempels (Eds.), WEBIST 2016 : Proceedings of the 12th International conference on web information systems and technologies. Volume 2 (pp. 251-256). SCITEPRESS. https://doi.org/10.5220/0005879802510256
© INSTICC, 2016. This is an author's final draft version of an article whose final and definitive form has been published in the WEBIST Proceedings. Published by SCITEPRESS.
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 serendipitous items, which is a challenging task, as it is unclear what makes items serendipitous to a user and how to measure serendipity. The concept is difficult to investigate, as serendipity includes an emotional dimension and serendipitous encounters are very rare. In this paper, we discuss mentioned challenges, review definitions of serendipity and serendipity-oriented evaluation metrics. The goal of the paper is to guide and inspire future efforts on serendipity in recommender systems.
Parent publication ISBN978-989-758-186-1
ConferenceInternational conference on web information systems and technologies
Is part of publicationWEBIST 2016 : Proceedings of the 12th International conference on web information systems and technologies. Volume 2
Publication in research information system
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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 ...
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; Veijalainen, Jari; Wang, Shuaiqiang (SCITEPRESS Science And Technology Publications, 2017)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 ...
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 ...