Overlapping Clustering of Contextual Bandits with NMF techniques
Kwon, Jin Kyoung
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We introduce a novel approach to recommendation based on item clustering using non-negative matrix factorization (NMF) techniques. We propose a new algorithm, OCB (Overlapping Clustering Bandits), that groups items into latent clusters using online user feedbacks and uses learned clusters to make recommendations. By making recommendation at cluster-level instead of at item-level, the algorithm can overcome scalability issues associated with a large number of items without compensating for long-term reward maximization. Also, by performing online clustering of items, the algorithm can learn latent topics associated with items based on user feedbacks.