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
Citation
NGUYEN, Trong T. and LAUW, Hady Wirawan.
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
Creative Commons 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