Regret vs. Bandwidth Trade-off for Recommendation Systems

TL;DR

This paper explores the fundamental tradeoff between regret and bandwidth in wireless recommendation systems, analyzing scenarios with contextual bandits and latent message structures to optimize performance under transmission constraints.

Contribution

It introduces a tight tradeoff characterization for regret and bandwidth in recommendation systems with wireless constraints, considering both contextual bandits and latent message structures.

Findings

Established a tight tradeoff between regret and bandwidth in certain instances.

Analyzed the impact of latent message structures on reducing learning phase.

Provided insights into optimizing recommendation systems under wireless constraints.

Abstract

We consider recommendation systems that need to operate under wireless bandwidth constraints, measured as number of broadcast transmissions, and demonstrate a (tight for some instances) tradeoff between regret and bandwidth for two scenarios: the case of multi-armed bandit with context, and the case where there is a latent structure in the message space that we can exploit to reduce the learning phase.