A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Diversity in news recommendations using contextual bandits (2022)

Semenov, Alexander, Rysz Maciej, Pandey, Gaurav, Xu, Guanglin. (2022). Diversity in news recommendations using contextual bandits. Expert Systems with Applications, 195, Article 116478. https://doi.org/10.1016/j.eswa.2021.116478

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Semenov, Alexander; Rysz Maciej; Pandey, Gaurav; Xu, Guanglin

Lehti tai sarja: Expert Systems with Applications

ISSN: 0957-4174

eISSN: 1873-6793

Julkaisuvuosi: 2022

Volyymi: 195

Artikkelinumero: 116478

Kustantaja: Elsevier BV

Julkaisumaa: Britannia

Julkaisun kieli: englanti

DOI: https://doi.org/10.1016/j.eswa.2021.116478

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Tiivistelmä

Contextual bandit techniques have recently been used for generating personalized user recommendations in situations where collaborative filtering based algorithms may be inefficient. They are often used in cases when input data are dynamically changing as new users and content items, such as news, constantly change. Contextual bandit methods sequentially select articles for recommendation to a user and continuously modify their strategies so as to present users with articles that maximize clicks. However, exclusively focusing on maximizing the number of clicks can lead to over-exposure of certain articles, while under-representing others. In an era of ever growing demand for digital news delivery, this, in turn, invokes the important notion of presenting news content to users in a “socially responsible” way. To this effect, we introduce a technique based on the contextual bandit framework that, in addition to maximization of the click rate, also considers historical frequency of an article as the “cost” associated with recommending it. It is demonstrated that this approach results in a more balanced distribution and a diverse set of recommended articles. Experiments utilizing a benchmark news dataset demonstrate the trade-off between clicks and diversity of recommended articles.

YSO-asiasanat: suosittelujärjestelmät; kontekstuaalisuus; koneoppiminen; uutiset; algoritmit

Vapaat asiasanat: recommender systems; contextual bandit; fairness; equitable machine learning

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2022

JUFO-taso: 1