Explore, exploit, and explain-Reference-Cited by-同舟云学术

Explore, exploit, and explain

Published:2018-09-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th ACM Conference on Recommender Systems
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Author:

McInerney James¹,Lacker Benjamin¹,Hansen Samantha¹,Higley Karl¹,Bouchard Hugues¹,Gruson Alois¹,Mehrotra Rishabh¹

Affiliation:

1. Spotify

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3240323.3240354

Reference25 articles.

1. TasteWeights

2. Allison J. B. Chaney Brandon Stewart and Barbara Engelhardt. 2017. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. arXiv preprint arXiv:1710.11214 (2017). Allison J. B. Chaney Brandon Stewart and Barbara Engelhardt. 2017. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. arXiv preprint arXiv:1710.11214 (2017).

3. Deep Neural Networks for YouTube Recommendations

4. Miroslav Dudik John Langford and Lihong Li. 2011. Doubly robust policy evaluation and learning. arXiv preprint arXiv:1103.4601 (2011). Miroslav Dudik John Langford and Lihong Li. 2011. Doubly robust policy evaluation and learning. arXiv preprint arXiv:1103.4601 (2011).

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