# Coordinated Online Learning With Applications to Learning User Preferences

## TL;DR

This paper introduces COOL, a novel online multi-task learning algorithm that coordinates related task learners via convex projections, improving learning efficiency and performance in applications like user preference modeling.

## Contribution

The paper proposes COOL, a new algorithm for coordinating online learners across related tasks using convex projections, with theoretical regret bounds and practical application to user preference learning.

## Key findings

- COOL effectively coordinates multi-task online learners.
- The algorithm achieves favorable regret bounds influenced by coordination parameters.
- Application to Airbnb preferences demonstrates practical utility.

## Abstract

We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners. We consider an algorithmic framework to model the relationship of these tasks via a set of convex constraints. To exploit this relationship, we design a novel algorithm -- COOL -- for coordinating the individual online learners: Our key idea is to coordinate their parameters via weighted projections onto a convex set. By adjusting the rate and accuracy of the projection, the COOL algorithm allows for a trade-off between the benefit of coordination and the required computation/communication. We derive regret bounds for our approach and analyze how they are influenced by these trade-off factors. We apply our results on the application of learning users' preferences on the Airbnb marketplace with the goal of incentivizing users to explore under-reviewed apartments.