Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
Author:
Affiliation:
1. Nanyang Technological University, Singapore, Singapore
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3583780.3614801
Reference83 articles.
1. Himan Abdollahpouri. 2019. Popularity Bias in Ranking and Recommendation. In AIES. 529--530. Himan Abdollahpouri. 2019. Popularity Bias in Ranking and Recommendation. In AIES. 529--530.
2. Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2017. Controlling popularity bias in learning-to-rank recommendation. In RecSys. 42--46. Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2017. Controlling popularity bias in learning-to-rank recommendation. In RecSys. 42--46.
3. Himan Abdollahpouri and Masoud Mansoury . 2020. Multi-sided Exposure Bias in Recommendation. CoRR , Vol. abs/ 2006 .15772 ( 2020 ). showeprint[arXiv]2006.15772 Himan Abdollahpouri and Masoud Mansoury. 2020. Multi-sided Exposure Bias in Recommendation. CoRR, Vol. abs/2006.15772 (2020). showeprint[arXiv]2006.15772
4. Himan Abdollahpouri , Masoud Mansoury , Robin Burke , and Bamshad Mobasher . 2019. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286 ( 2019 ). Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286 (2019).
5. Privacy-Preserving Machine Learning: Threats and Solutions
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