Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering-Reference-Cited by-同舟云学术

Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:The World Wide Web Conference on - WWW '19
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Author:

Chae Dong-Kyu¹,Kang Jin-Soo¹,Kim Sang-Wook¹,Choi Jaeho²

Affiliation:

1. Hanyang University, Republic of Korea

2. Naver Corporation, Republic of Korea

Publisher

ACM Press

Reference35 articles.

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2. Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan. 2017. Unsupervised pixel-level domain adaptation with generative adversarial networks. In Proceedings of the IEEE international conference on computer vision and pattern recognition, Vol. 1. 7.

3. Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018. CFGAN: A generic collaborative filtering framework based on generative adversarial networks. In Proceedings of the 27th ACM international conference on information and knowledge management. 137-146.

4. Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F Stewart, and Jimeng Sun. 2017. Generating multi-label discrete electronic health records using generative adversarial networks. arXiv preprint arXiv:1703.06490(2017).

5. Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the 4th ACM conference on recommender systems. 39-46.

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