Alternating Linear Bandits for Online Matrix-Factorization Recommendation

TL;DR

This paper introduces a new online matrix factorization algorithm for recommendation systems that combines linear bandits with alternating least squares, demonstrating superior performance in synthetic and real-world datasets.

Contribution

The paper proposes a novel online matrix factorization method integrating linear bandits and alternating least squares, improving recommendation accuracy.

Findings

Outperforms existing online algorithms in synthetic datasets

Achieves lower cumulative regret and higher NDCG scores

Effective in real-world online collaborative filtering scenarios

Abstract

We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by the agent) and provides a rating of the selected item. In this paper, we propose a novel algorithm for online matrix factorization recommendation that combines linear bandits and alternating least squares. In this formulation, the bandit feedback is equal to the difference between the ratings of the best and selected items. We evaluate the performance of the proposed algorithm over time using both cumulative regret and average cumulative NDCG. Simulation results over three synthetic datasets as well as three real-world datasets for online collaborative filtering indicate the superior performance of the proposed algorithm over two state-of-the-art online algorithms.