A Gang of Bandits
Nicol\`o Cesa-Bianchi, Claudio Gentile, Giovanni Zappella

TL;DR
This paper introduces network-aware bandit algorithms that leverage social relationships among users to improve recommendation performance, demonstrating significant gains over traditional methods through experiments on synthetic and real data.
Contribution
It presents novel algorithms for networked bandit problems, incorporating social graph information to enhance exploration and exploitation strategies.
Findings
Networked bandit algorithms outperform traditional methods.
Exploiting social relationships improves prediction accuracy.
Variants based on clustering scale well to large networks.
Abstract
Multi-armed bandit problems are receiving a great deal of attention because they adequately formalize the exploration-exploitation trade-offs arising in several industrially relevant applications, such as online advertisement and, more generally, recommendation systems. In many cases, however, these applications have a strong social component, whose integration in the bandit algorithm could lead to a dramatic performance increase. For instance, we may want to serve content to a group of users by taking advantage of an underlying network of social relationships among them. In this paper, we introduce novel algorithmic approaches to the solution of such networked bandit problems. More specifically, we design and analyze a global strategy which allocates a bandit algorithm to each network node (user) and allows it to "share" signals (contexts and payoffs) with the neghboring nodes. We then…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Data Stream Mining Techniques · Recommender Systems and Techniques
