Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

Author:

McDonald Thomas M.1ORCID,Maystre Lucas2ORCID,Lalmas Mounia2ORCID,Russo Daniel3ORCID,Ciosek Kamil2ORCID

Affiliation:

1. University of Manchester, Manchester, United Kingdom

2. Spotify, London, United Kingdom

3. University of Columbia & Spotify, New York, NY, USA

Publisher

ACM

Reference44 articles.

1. Shipra Agrawal and Navin Goyal . 2012 . Analysis of Thompson sampling for the multi-armed bandit problem . In Conference on learning theory. JMLR Workshop and Conference Proceedings, 39--1. Shipra Agrawal and Navin Goyal. 2012. Analysis of Thompson sampling for the multi-armed bandit problem. In Conference on learning theory. JMLR Workshop and Conference Proceedings, 39--1.

2. Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy

3. Meta Dynamic Pricing: Transfer Learning Across Experiments

4. No regrets for learning the prior in bandits;Basu Soumya;Advances in Neural Information Processing Systems,2021

5. James Bennett and Stan Lanning . 2007 . The Netflix Prize . In Proceedings of KDDCup '07 . San Jose, CA, USA. James Bennett and Stan Lanning. 2007. The Netflix Prize. In Proceedings of KDDCup '07. San Jose, CA, USA.

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