Fast Distributed Bandits for Online Recommendation Systems

Contextual bandit algorithms are commonly used in recommender systems, where content popularity can change rapidly. These algorithms continuously learn good mappings between users and items, based on contexts associated with both the users and items. 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-ofthe-art distributed bandit algorithm DCCB relies on a peerto-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 we propose 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. Secondly, DistCLUB finds clusters much faster, achieving better accuracy than the state-of-the-art algorithm. Evaluation over both real-world benchmark and synthetic datasets show that DistCLUB is on average 8.87x faster than DCCB, and achieves 14.5% higher prediction performance.

• Distributed Systems and Parallel Computing

• Algorithms and Theory