Top-N Recommendation Algorithms: A Quest for the State-of-the-Art-Reference-Cited by-同舟云学术

Top-N Recommendation Algorithms: A Quest for the State-of-the-Art

Published:2022-07-04 Issue: Volume: Page:
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Container-title:Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
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Author:

Anelli Vito Walter¹,Bellogín Alejandro²,Di Noia Tommaso¹,Jannach Dietmar³,Pomo Claudio¹

Affiliation:

1. Politecnico di Bari, Italy

2. Universidad Autonoma de Madrid, Spain

3. University of Klagenfurt, Austria

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3503252.3531292

Reference39 articles.

1. Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2019. Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking. In FLAIRS ’19. 413–418. Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2019. Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking. In FLAIRS ’19. 413–418.

2. Vito Walter Anelli Alejandro Bellogín Antonio Ferrara Daniele Malitesta Felice Antonio Merra Claudio Pomo Francesco Maria Donini and Tommaso Di Noia. 2021. Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR ’21. 2405–2414. Vito Walter Anelli Alejandro Bellogín Antonio Ferrara Daniele Malitesta Felice Antonio Merra Claudio Pomo Francesco Maria Donini and Tommaso Di Noia. 2021. Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR ’21. 2405–2414.

3. On the discriminative power of hyper-parameters in cross-validation and how to choose them

4. Improvements that don't add up

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