Spectral bandits for smooth graph functions

TL;DR

This paper introduces spectral bandit algorithms for smooth graph functions, enabling efficient online learning and recommendation with regret bounds that scale with a small effective dimension.

Contribution

It proposes new algorithms leveraging the effective dimension of graphs, improving scalability in online learning tasks involving graph-structured data.

Findings

Algorithms scale linearly and sublinearly with the effective dimension.

Experiments show effective preference learning from limited node evaluations.

The framework applies to content recommendation with high accuracy from few samples.

Abstract

Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each item we can recommend is a node and its expected rating is similar to its neighbors. The goal is to recommend items that have high expected ratings. We aim for the algorithms where the cumulative regret with respect to the optimal policy would not scale poorly with the number of nodes. In particular, we introduce the notion of an effective dimension, which is small in real-world graphs, and propose two algorithms for solving our problem that scale linearly and sublinearly in this dimension. Our experiments on real-world content recommendation problem show that a good estimator of user preferences for thousands of items can be learned from just tens of nodes evaluations.