Fast distributed bandits for online recommendation systems

Kanak Mahadik, Qingyun Wu, Shuai Li, Amit Sabne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

49 Scopus citations


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 network 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 34th ACM International Conference on Supercomputing, ICS 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450379830
StatePublished - Jun 29 2020
Event34th ACM International Conference on Supercomputing, ICS 2020 - Barcelona, Spain
Duration: Jun 29 2020Jul 2 2020

Publication series

NameProceedings of the International Conference on Supercomputing


Conference34th ACM International Conference on Supercomputing, ICS 2020

All Science Journal Classification (ASJC) codes

  • General Computer Science


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