Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering-Reference-Cited by-同舟云学术

Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering

Published:2019-05-13 Issue: Volume: Page:
ISSN:
Container-title:Companion Proceedings of The 2019 World Wide Web Conference
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Author:

Sun Wenlong¹,Khenissi Sami¹,Nasraoui Olfa¹,Shafto Patrick²

Affiliation:

1. University of Louisville

2. Rutgers University, NJ

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3308560.3317303

Reference18 articles.

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

2. Allison JB Chaney Brandon M Stewart and Barbara E Engelhardt. 2017. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. arXiv preprint arXiv:1710.11214(2017). Allison JB Chaney Brandon M Stewart and Barbara E Engelhardt. 2017. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. arXiv preprint arXiv:1710.11214(2017).

3. Rank aggregation via nuclear norm minimization

4. Collaborative Filtering for Implicit Feedback Datasets

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