2014 NetworkedBanditswithDisjointLin

• (Fang & Tao, 2014) ⇒ Meng Fang, and Dacheng Tao. (2014). “Networked Bandits with Disjoint Linear Payoffs.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623672

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Networked+Bandits+with+Disjoint+Linear+Payoffs
• http://dl.acm.org/citation.cfm?id=2623330.2623672&preflayout=flat#citedby

Quotes

Author Keywords

• Exploration/exploitation dilemma; learning; networked bandits; on-line information services; social network

Abstract

In this paper, we study `networked bandits', a new bandit problem where a set of interrelated arms varies over time and, given the contextual information that selects one arm, invokes other correlated arms. This problem remains under-investigated, in spite of its applicability to many practical problems. For instance, in social networks, an arm can obtain payoffs from both the selected user and its relations since they often share the content through the network. We examine whether it is possible to obtain multiple payoffs from several correlated arms based on the relationships. In particular, we formalize the networked bandit problem and propose an algorithm that considers not only the selected arm, but also the relationships between arms. Our algorithm is `optimism in face of uncertainty' style, in that it decides an arm depending on integrated confidence sets constructed from historical data. We analyze the performance in simulation experiments and on two real-world offline datasets. The experimental results demonstrate our algorithm's effectiveness in the networked bandit setting.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 NetworkedBanditswithDisjointLin	Dacheng Tao Meng Fang			Networked Bandits with Disjoint Linear Payoffs				10.1145/2623330.2623672		2014