Online Clustering of Bandits

By Claudio Gentile, Shuai Li, Giovanni Zappella. 2014

289 citations recorded by the source corpus.

We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation "bandit") strategies. We provide a sharp regret analysis of this algorithm in a standard stochastic noise setting, demonstrate its scalability properties, and prove its effectiveness on a number of artificial and real-world datasets. Our experiments show a significant increase in prediction performance over state-of-the-art methods for bandit problems.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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Citations in the corpus

DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, background): “Recently, some people try to combine bandit with clustering based collaborative filtering [14], and matrix factorization [6, 21, 32, 42, 43, 51], in order to model more complex user and item relationship, and utilize the social network relationship in determining the reward function.”
Contextual Bandits with Latent Confounders: An NMF Approach (2016, background): “Another related line of work is in the online clustering of bandits [17, 31, 30].”
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation (2015, unknown): “Kocak et al. [15] combine the spectral bandit algorithm in [14] with TS. Gentile et al. [16] propose a bandit algorithm for recommendations that clusters users in an online fashion based on the similarity of their preferences.”
Factorization Bandits for Interactive Recommendation (2017, background): “…among a set of contextual bandits over users via a graph Laplacian based model regularization, but it cannot estimate the latent factors; 4) CLUB (Gentile, Li, and Zappella 2014), which clusters users during online learning to enable model sharing; but it only works with contextual features; 5)…”
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods (2016, methodology): “Gentile et al. [9] consider the more general scenario of latent contextual bandits, where the contexts belong to a few underlying hidden classes.”
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems (2020, background): “question [11, 14, 21, 24, 37], the issue has largely been unaddressed in the context of conversational recommender system.”