Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives-Reference-Cited by-同舟云学术

Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives

Published:2023-09-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM Conference on Recommender Systems
language:
Short-container-title:

Author:

Paparella Vincenzo¹^ORCID,Anelli Vito Walter¹^ORCID,Boratto Ludovico²^ORCID,Di Noia Tommaso¹^ORCID

Affiliation:

1. Department of Electrical and Information Engineering, Politecnico di Bari, Italy

2. Department of Mathematics and Computer Science, University of Cagliari, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604915.3609493

Reference38 articles.

1. Multistakeholder recommendation: Survey and research directions

2. Controlling Popularity Bias in Learning-to-Rank Recommendation

3. Himan Abdollahpouri , Masoud Mansoury , Robin Burke , and Bamshad Mobasher . 2019. The Unfairness of Popularity Bias in Recommendation . In Proceedings of the Workshop on Recommendation in Multi-stakeholder Environments co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019 ), Copenhagen, Denmark, September 20, 2019(CEUR Workshop Proceedings, Vol. 2440), Robin Burke, Himan Abdollahpouri, Edward C. Malthouse , K. P. Thai, and Yongfeng Zhang (Eds.). CEUR-WS.org. https://ceur-ws.org/Vol-2440/paper4.pdf Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The Unfairness of Popularity Bias in Recommendation. In Proceedings of the Workshop on Recommendation in Multi-stakeholder Environments co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019), Copenhagen, Denmark, September 20, 2019(CEUR Workshop Proceedings, Vol. 2440), Robin Burke, Himan Abdollahpouri, Edward C. Malthouse, K. P. Thai, and Yongfeng Zhang (Eds.). CEUR-WS.org. https://ceur-ws.org/Vol-2440/paper4.pdf

4. MORS 2022: The Second Workshop on Multi-Objective Recommender Systems

5. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. User Perceptions of Diversity in Recommender Systems;Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-22