Thompson Sampling For Stochastic Bandits with Graph Feedback
This work addresses decision-making in networked environments for applications like social networks or recommendation systems, representing an incremental improvement by adapting an existing method to handle graph feedback without prior knowledge.
The paper tackles the problem of stochastic sequential decision-making with graph feedback by extending Thompson Sampling to unknown or changing graph structures, achieving theoretical Bayesian regret guarantees and demonstrating through experiments on real and simulated networks that it outperforms related upper confidence bound methods.
We present a novel extension of Thompson Sampling for stochastic sequential decision problems with graph feedback, even when the graph structure itself is unknown and/or changing. We provide theoretical guarantees on the Bayesian regret of the algorithm, linking its performance to the underlying properties of the graph. Thompson Sampling has the advantage of being applicable without the need to construct complicated upper confidence bounds for different problems. We illustrate its performance through extensive experimental results on real and simulated networks with graph feedback. More specifically, we tested our algorithms on power law, planted partitions and Erdo's-Renyi graphs, as well as on graphs derived from Facebook and Flixster data. These all show that our algorithms clearly outperform related methods that employ upper confidence bounds, even if the latter use more information about the graph.