Graph Clustering Bandits for Recommendation
This work addresses the challenge of dynamic user clustering for personalized recommendations, offering an incremental improvement in sequential clustering techniques.
The paper tackles the problem of efficiently clustering users in recommender systems using multi-armed bandits, resulting in improved prediction performance and scalability over state-of-the-art methods on real-world datasets.
We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users. Our algorithm dynamically groups users based on their observed behavioral similarity during a sequence of logged activities. In doing so, the algorithm reacts to the currently served user by shaping clusters around him/her but, at the same time, it explores the generation of clusters over users which are not currently engaged. We motivate the effectiveness of this clustering policy, and provide an extensive empirical analysis on real-world datasets, showing scalability and improved prediction performance over state-of-the-art methods for sequential clustering of users in multi-armed bandit scenarios.