# Alternating Linear Bandits for Online Matrix-Factorization Recommendation

arXiv: https://arxiv.org/abs/1810.09401 · HTML: https://mlsanity.com/paper/1810.09401/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [Optimal Algorithms for Latent Bandits with Cluster Structure](https://mlsanity.com/paper/2301.07040/) (2023)
- [Online Low Rank Matrix Completion](https://mlsanity.com/paper/2209.03997/) (2022)
- [Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health](https://mlsanity.com/paper/2507.16356/) (2025)
- [Online Matrix Completion: A Collaborative Approach with Hott Items](https://mlsanity.com/paper/2408.05843/) (2024)
- [Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program](https://mlsanity.com/paper/2410.21405/) (2024)
- [The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems](https://mlsanity.com/paper/2604.26651/) (2026)

## Abstract (arXiv)

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.