Conference Proceeding Article
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.
exploitation and exploration, multi-armed bandit, clustering
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
CIKM'14: Proceedings of the 2014 ACM International Conference on Information and Knowledge Management: November 3-7, 2014, Shanghai, China
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NGUYEN, Trong T. and LAUW, Hady W..
Dynamic Clustering of Contextual Multi-Armed Bandits. (2014). CIKM'14: Proceedings of the 2014 ACM International Conference on Information and Knowledge Management: November 3-7, 2014, Shanghai, China. 1959-1962. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2328
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