A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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. https://doi.org/10.1007/s00607-018-0687-5

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Kotkov, Denis; Veijalainen, Jari; Wang, Shuaiqiang

Lehti tai sarja: Computing

ISSN: 0010-485X

eISSN: 1436-5057

Julkaisuvuosi: 2020

Volyymi: 102

Lehden numero: 2

Artikkelin sivunumerot: 393-411

Kustantaja: Springer Wien

Julkaisumaa: Itävalta

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/67800

Tiivistelmä

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.

YSO-asiasanat: suosittelujärjestelmät; algoritmit; arviointi

Vapaat asiasanat: learning to rank; serendipity; novelty; unexpectedness; serendipity-2018

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 2