Generalized Low-Rank Matrix Contextual Bandits with Graph Information

TL;DR

This paper introduces a novel matrix contextual bandit algorithm that effectively combines low-rank structures with graph information, improving decision-making performance in applications like recommender systems.

Contribution

It proposes a unified framework integrating low-rank and graph data into matrix CB, with theoretical regret bounds and practical validation.

Findings

Outperforms existing methods in cumulative regret bounds.

Effectively utilizes graph information for better decision policies.

Validated on synthetic and real-world datasets.

Abstract

The matrix contextual bandit (CB), as an extension of the well-known multi-armed bandit, is a powerful framework that has been widely applied in sequential decision-making scenarios involving low-rank structure. In many real-world scenarios, such as online advertising and recommender systems, additional graph information often exists beyond the low-rank structure, that is, the similar relationships among users/items can be naturally captured through the connectivity among nodes in the corresponding graphs. However, existing matrix CB methods fail to explore such graph information, and thereby making them difficult to generate effective decision-making policies. To fill in this void, we propose in this paper a novel matrix CB algorithmic framework that builds upon the classical upper confidence bound (UCB) framework. This new framework can effectively integrate both the low-rank structure and graph information in a unified manner. Specifically, it involves first solving a joint nuclear norm and matrix Laplacian regularization problem, followed by the implementation of a graph-based generalized linear version of the UCB algorithm. Rigorous theoretical analysis demonstrates that our procedure outperforms several popular alternatives in terms of cumulative regret bound, owing to the effective utilization of graph information. A series of synthetic and real-world data experiments are conducted to further illustrate the merits of our procedure.