# Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program

arXiv: https://arxiv.org/abs/2410.21405 · HTML: https://mlsanity.com/paper/2410.21405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Optimal Best Arm Identification with Fixed Confidence](https://mlsanity.com/paper/1602.04589/) (2016)
- [Model-based Reinforcement Learning and the Eluder Dimension](https://mlsanity.com/paper/1406.1853/) (2014)
- [Boltzmann Exploration Done Right](https://mlsanity.com/paper/1705.10257/) (2017)
- [Regret Guarantees for Item-Item Collaborative Filtering](https://mlsanity.com/paper/1507.05371/) (2015)
- [Alternating Linear Bandits for Online Matrix-Factorization Recommendation](https://mlsanity.com/paper/1810.09401/) (2018)
- [ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria](https://mlsanity.com/paper/2204.13663/) (2022)
- [Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC](https://mlsanity.com/paper/1503.01596/) (2015)
- [Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation](https://mlsanity.com/paper/2311.07139/) (2023)
- [Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering](https://mlsanity.com/paper/2002.03703/) (2020)

## Abstract (arXiv)

Mobile health (mHealth) programs face a critical challenge in optimizing the timing of automated health information calls to beneficiaries. This challenge has been formulated as a collaborative multi-armed bandit problem, requiring online learning of a low-rank reward matrix. Existing solutions often rely on heuristic combinations of offline matrix completion and exploration strategies. In this work, we propose a principled Bayesian approach using Thompson Sampling for this collaborative bandit problem. Our method leverages prior information through efficient Gibbs sampling for posterior inference over the low-rank matrix factors, enabling faster convergence. We demonstrate significant improvements over state-of-the-art baselines on a real-world dataset from the world's largest maternal mHealth program. Our approach achieves a 16% reduction in the number of calls compared to existing methods and a 47% reduction compared to the deployed random policy. This efficiency gain translates to a potential increase in program capacity by 0.5-1.4 million beneficiaries, granting them access to vital ante-natal and post-natal care information. Furthermore, we observe a 7% and 29% improvement in beneficiary retention (an extremely hard metric to impact) compared to state-of-the-art and deployed baselines, respectively. Synthetic simulations further demonstrate the superiority of our approach, particularly in low-data regimes and in effectively utilizing prior information. We also provide a theoretical analysis of our algorithm in a special setting using Eluder dimension.