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Enhancing Recommendation Quality of the SASRec Model by Mitigating Popularity Bias

Published: 08 October 2024 Publication History

Abstract

ZDF is a Public Service Media (PSM) broadcaster in Germany that uses recommender systems on its streaming service platform ZDFmediathek. One of the main use cases within the ZDFmediathek is Next Video, which is currently based on a Self-Attention based Sequential Recommendation model (SASRec). For this use case, we modified the loss function, the sampling method of negative items, and introduced the top-k negative sampling strategy and compared this to the vanilla SASRec model. We show that this not only reduces popularity bias, but also increases clicks and viewing volume compared to that of the vanilla version.

References

[1]
Andreas Grün and Xenija Neufeld. 2022. Translating the Public Service Media Remit into Metrics and Algorithms. In Proceedings of the 16th ACM Conference on Recommender Systems. 460–463.
[2]
Andreas Grün and Xenija Neufeld. 2023. Transparently Serving the Public: Enhancing Public Service Media Values through Exploration. In Proceedings of the 17th ACM Conference on Recommender Systems. 1045–1048.
[3]
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).
[4]
Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197–206.
[5]
Aleksandr Vladimirovich Petrov and Craig Macdonald. 2023. gsasrec: Reducing overconfidence in sequential recommendation trained with negative sampling. In Proceedings of the 17th ACM Conference on Recommender Systems. 116–128.
[6]
Timo Wilm, Philipp Normann, Sophie Baumeister, and Paul-Vincent Kobow. 2023. Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions. In Proceedings of the 17th ACM Conference on Recommender Systems. 1023–1026.

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cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2024

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Author Tags

  1. Public Remit
  2. Public Service Media
  3. SASRec
  4. bias
  5. personalization
  6. popularity
  7. recommender systems

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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