University of Oulu

Elahi, M., Kholgh, D. K., Kiarostami, M. S., Saghari, S., Rad, S. P., & Tkalčič, M. (2021). Investigating the impact of recommender systems on user-based and item-based popularity bias. Information Processing & Management, 58(5), 102655. https://doi.org/10.1016/j.ipm.2021.102655

Investigating the impact of recommender systems on user-based and item-based popularity bias

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Author: Elahi, Mehdi1; Kholgh, Danial Khosh; Kiarostami, Mohammad Sina2;
Organizations: 1University of Bergen, Bergen, Norway
2University of Oulu, Oulu, Finland
3University of Primorska, Koper, Slovenia
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2022022821123
Language: English
Published: Elsevier, 2021
Publish Date: 2023-06-21
Description:

Abstract

Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular.

In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score.

The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages.

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Series: Information processing & management
ISSN: 0306-4573
ISSN-E: 1873-5371
ISSN-L: 0306-4573
Volume: 58
Issue: 5
Article number: 102655
DOI: 10.1016/j.ipm.2021.102655
OADOI: https://oadoi.org/10.1016/j.ipm.2021.102655
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This work was supported by industry partners, Norway and the Research Council of Norway with funding to MediaFutures: Research Centre for Responsible Media Technology and Innovation, through The Centres for Research-based Innovation scheme, project number 309339.
Copyright information: © 2021 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/