Controlling Popularity Bias in Sequential Recommendation Models
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63223-5_8
Reference10 articles.
1. Rhee, W., Cho, S.M., Suh, B.: Countering popularity bias by regularizing score differences. In: Proceedings of the 16th ACM Conference on Recommender Systems, Seattle, WA, USA, pp. 145–155 (2022). https://doi.org/10.1145/3523227.3546757
2. Pellegrini, R., Zhao, W., Murray, I.: Don’t recommend the obvious: estimate probability ratios. In: Proceedings of the 16th ACM Conference on Recommender Systems, Seattle, WA, USA, pp. 188–197 (2022). https://doi.org/10.1145/3523227.3546753
3. Baran, B., et al.: Accelerating creator audience building through centralized exploration. In: Proceedings of the 17th ACM Conference on Recommender Systems, Singapore, Singapore, pp. 70–73 (2023). https://doi.org/10.1145/3604915.3608880
4. Vaswani, A., et al.: Attention is all you need. In: 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, pp. 6000–6010 (2017). https://doi.org/10.5555/3295222.3295349
5. Kang, W., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining, Singapore, Singapore, pp. 197–206 (2018). https://doi.org/10.1109/ICDM.2018.00035
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