Pricing the Long Tail by Explainable Product Aggregation and Monotonic Bandits

Author:

Mussi Marco1,Genalti Gianmarco1,Trovò Francesco1,Nuara Alessandro2,Gatti Nicola1,Restelli Marcello1

Affiliation:

1. Politecnico di Milano, Milano, Italy

2. ML cube, Milano, Italy

Publisher

ACM

Reference47 articles.

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2. Hammaad Adam , Pu He , and Fanyin Zheng . 2020. Machine Learning for Demand Estimation in Long Tail Markets. Columbia Business School Research Paper Forthcoming -, - ( 2020 ), 1--43. Hammaad Adam, Pu He, and Fanyin Zheng. 2020. Machine Learning for Demand Estimation in Long Tail Markets. Columbia Business School Research Paper Forthcoming -, - (2020), 1--43.

3. Shipra Agrawal and Navin Goyal . 2012 . Analysis of Thompson Sampling for the Multi-armed Bandit Problem. In Conference on Learning Theory (COLT) , Vol. 23 . JMLR Workshop and Conference Proceedings, 39.1--39.26. Shipra Agrawal and Navin Goyal. 2012. Analysis of Thompson Sampling for the Multi-armed Bandit Problem. In Conference on Learning Theory (COLT), Vol. 23. JMLR Workshop and Conference Proceedings, 39.1--39.26.

4. Chris Anderson. 2004. The Long Tail. https://www.wired.com/2004/10/tail/ Chris Anderson. 2004. The Long Tail. https://www.wired.com/2004/10/tail/

5. Chris Anderson . 2006. The long tail: Why the future of business is selling less of more . Hachette Books . Chris Anderson. 2006. The long tail: Why the future of business is selling less of more. Hachette Books.

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