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Learning to Interact with Users: A Collaborative-Bandit Approach

Learning to interact with users and discover their preferences is central in most web applications, with recommender systems being a notable example. From such a perspective, merging interactive learning algorithms with recommendation models is natural. While recent literature has explored the idea of combining collaborative filtering approaches with bandit techniques, there exist two limitations: (1) they usually consider Gaussian rewards, which are not suitable for implicit feedback data powering most recommender systems, and (2) they are restricted to the one-item recommendation setting while typically a list of recommendations is given. In this paper, to address these limitations, apart from Gaussian rewards we also consider Bernoulli rewards, the latter being suitable for dyadic data. Also, we consider two user click models: the one-item click/no-click model, and the cascade click model which is suitable for top-K recommendations. For these settings, we propose novel machine learning algorithms that learn to interact with users by learning the underlying parameters collaboratively across users and items. We provide an extensive empirical study, which is the first to illustrate all pairwise empirical comparisons across different interactive learning algorithms for recommendation. Our experiments demonstrate that when the number of users and items is large, propagating the feedback across users and items while learning latent features is the most effective approach for systems to learn to interact with the users.