Publication Type

Conference Proceeding Article

Publication Date

11-2014

Abstract

With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge. Multi-armed bandit (MAB) is a framework to balance this trade-off. Most of the previous work in MAB either models a single bandit for the whole population, or one bandit for each user. We propose an algorithm to divide the population of users into multiple clusters, and to customize the bandits to each cluster. This clustering is dynamic, i.e., users can switch from one cluster to another, as their preferences change. We evaluate the proposed algorithm on two real-life datasets.

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

CIKM'14: Proceedings of the 2014 ACM International Conference on Information and Knowledge Management: November 3-7, 2014, Shanghai, China

First Page

1959

Last Page

1962

ISBN

9781450325981

Identifier

10.1145/2661829.2662063

Publisher

ACM

City or Country

New York

Copyright Owner and License

LARC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1145/2661829.2662063

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