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• W2324996585 abstract "Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting." @default.
• W2324996585 created "2016-06-24" @default.
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• W2324996585 date "2015-02-11" @default.
• W2324996585 modified "2023-09-27" @default.
• W2324996585 title "Collaborative Filtering Bandits" @default.
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• W2324996585 doi "https://doi.org/10.48550/arxiv.1502.03473" @default.
• W2324996585 hasPublicationYear "2015" @default.
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