Fast Distributed Bandits for Online Recommendation Systems

TL;DR

This paper introduces DistCLUB, a scalable distributed bandit algorithm for online recommendation systems that quickly discovers user clusters and significantly improves recommendation accuracy and speed over existing methods.

Contribution

DistCLUB is a novel distributed bandit algorithm that lazily creates clusters, reducing network load and accelerating cluster discovery compared to prior algorithms like DCCB.

Findings

DistCLUB is on average 8.87x faster than DCCB.

DistCLUB achieves 14.5% higher normalized prediction performance.

DistCLUB scales better with increasing users and items.

Abstract

Contextual bandit algorithms are commonly used in recommender systems, where content popularity can change rapidly. These algorithms continuously learn latent mappings between users and items, based on contexts associated with them both. Recent recommendation algorithms that learn clustering or social structures between users have exhibited higher recommendation accuracy. However, as the number of users and items in the environment increases, the time required to generate recommendations deteriorates significantly. As a result, these cannot be deployed in practice. The state-of-the-art distributed bandit algorithm - DCCB - relies on a peer-to-peer net-work to share information among distributed workers. However, this approach does not scale well with the increasing number of users. Furthermore, it suffers from slow discovery of clusters, resulting in accuracy degradation. To address the above issues, this paper proposes a novel distributed bandit-based algorithm called DistCLUB. This algorithm lazily creates clusters in a distributed manner, and dramatically reduces the network data sharing requirement, achieving high scalability. Additionally, DistCLUB finds clusters much faster, achieving better accuracy than the state-of-the-art algorithm. Evaluation over both real-world benchmarks and synthetic datasets shows that DistCLUB is on average 8.87x faster than DCCB, and achieves 14.5% higher normalized prediction performance.