Pointing the way: active collaborative filtering
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An algorithmic framework for performing collaborative filtering
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A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
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EigenRank: a ranking-oriented approach to collaborative filtering
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Personalized active learning for collaborative filtering
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A contextual-bandit approach to personalized news article recommendation
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Exploitation and exploration in a performance based contextual advertising system
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On bootstrapping recommender systems
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Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
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Adaptive bootstrapping of recommender systems using decision trees
Proceedings of the fourth ACM international conference on Web search and data mining
Functional matrix factorizations for cold-start recommendation
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Adaptive diversification of recommendation results via latent factor portfolio
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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In this paper, we study collaborative filtering (CF) in an interactive setting, in which a recommender system continuously recommends items to individual users and receives interactive feedback. Whilst users enjoy sequential recommendations, the recommendation predictions are constantly refined using up-to-date feedback on the recommended items. Bringing the interactive mechanism back to the CF process is fundamental because the ultimate goal for a recommender system is about the discovery of interesting items for individual users and yet users' personal preferences and contexts evolve over time during the interactions with the system. This requires us not to distinguish between the stages of collecting information to construct the user profile and making recommendations, but to seamlessly integrate these stages together during the interactive process, with the goal of maximizing the overall recommendation accuracy throughout the interactions. This mechanism naturally addresses the cold-start problem as any user can immediately receive sequential recommendations without providing ratings beforehand. We formulate the interactive CF with the probabilistic matrix factorization (PMF) framework, and leverage several exploitation-exploration algorithms to select items, including the empirical Thompson sampling and upper confidence bound based algorithms. We conduct our experiment on cold-start users as well as warm-start users with drifting taste. Results show that the proposed methods have significant improvements over several strong baselines for the MovieLens, EachMovie and Netflix datasets.