A1 Journal article (refereed)
How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm (2020)


Kotkov, D., Veijalainen, J., & Wang, S. (2020). How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing, 102 (2), 393-411. doi:10.1007/s00607-018-0687-5


JYU authors or editors


Publication details

All authors or editors: Kotkov, Denis; Veijalainen, Jari; Wang, Shuaiqiang

Journal or series: Computing

ISSN: 0010-485X

eISSN: 1436-5057

Publication year: 2020

Volume: 102

Issue number: 2

Pages range: 393-411

Publisher: Springer Wien

Publication country: Austria

Publication language: English

DOI: http://doi.org/10.1007/s00607-018-0687-5

Open Access: Open access publication published in a hybrid channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67800


Abstract

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, 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, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available dataset containing user feedback regarding serendipity. We compared our SOG algorithm with topic diversification, popularity baseline, singular value decomposition, serendipitous personalized ranking and Zheng’s algorithms relying on the above dataset. SOG outperforms other algorithms in terms of serendipity and diversity. It also outperforms serendipity-oriented algorithms in terms of accuracy, but underperforms accuracy-oriented algorithms in terms of accuracy. We found that the increase of diversity can hurt accuracy and harm or improve serendipity depending on the size of diversity increase.


Keywords: recommender systems; algorithms; evaluation

Free keywords: learning to rank; serendipity; novelty; unexpectedness; serendipity-2018


Contributing organizations

Other organizations:


Related projects

Tiedonkaivuu sosiaalisesta (MineSocMed)
Veijalainen, Jari
Academy of Finland
01/09/2013-31/08/2017


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 2


Last updated on 2020-18-10 at 20:26