Cited By
View all- Klimashevskaia AJannach DElahi MTrattner C(2024)A survey on popularity bias in recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09406-0Online publication date: 1-Jul-2024
Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models
Pages 7 - 11
Abstract
The rapid growth of the volume and variety of online media content has made it increasingly challenging for users to discover fresh content that meets their particular needs and tastes. Recommender Systems are digital tools that support users in navigating the plethora of available items. While these systems may offer several benefits, they may also create or reinforce certain undesired effects, including Popularity Bias, i.e., a short list of popular items becoming more popular while a long list of unpopular ones becoming even more unpopular.
In this paper, we focus on this challenge and propose a novel recommendation approach that can generate accurate recommendations while effectively mitigating the popularity bias. Our proposed approach adopts models based on Self-Supervised Learning (SSL) that have recently drawn considerable attention in various application domains. Such models are known to enable recommender systems to exploit automatic mechanisms for data annotation hence providing self-supervisory signals for better training of the system from the available data. We considered various recommendation techniques based on the SSL model and compared their impact on popularity bias mitigation measured in terms of Average Recommendation Popularity (ARP), Gini-index, and Coverage. The results showed that SSL models could successfully mitigate the popularity bias while still maintaining the accuracy of the recommendation.
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Index Terms
- Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models
Recommendations
An Enhanced Collaborative Filtering with Flexible Item Popularity Control for Recommender Systems
SocialCom '14: Proceedings of the 2014 International Conference on Social ComputingWith the emerging and rapid development of Internet applications like social networks, E-commerce and so on, massive information has been created and stored. Recommender systems have been developed to deal with the information overload problem. Various ...
An Enhanced Collaborative Filtering with Flexible Item Popularity Control for Recommender Systems
SocialCom '14: Proceedings of the 2014 International Conference on Social ComputingWith the emerging and rapid development of Internet applications like social networks, E-commerce and so on, massive information has been created and stored. Recommender systems have been developed to deal with the information overload problem. Various ...
Temporal diversity in recommender systems
SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrievalCollaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items ...
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June 2023
446 pages
ISBN:9781450398916
DOI:10.1145/3563359
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Published: 16 June 2023
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UMAP '23: 31st ACM Conference on User Modeling, Adaptation and Personalization
June 26 - 29, 2023
Limassol, Cyprus
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View all- Klimashevskaia AJannach DElahi MTrattner C(2024)A survey on popularity bias in recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09406-0Online publication date: 1-Jul-2024
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