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Managing recommendation data in large scale

Abstract : In this thesis, we address the scalability problem of recommender systems. We propose accu rate and scalable algorithms. We first consider the case of matrix factorization techniques in a dynamic context, where new ratings..are continuously produced. ln such case, it is not possible to have an up to date model, due to the incompressible time needed to compute it. This happens even if a distributed technique is used for matrix factorization. At least, the ratings produced during the model computation will be missing. Our solution reduces the loss of the quality of the recommendations over time, by introducing some stable biases which track users' behavior deviation. These biases are continuously updated with the new ratings, in order to maintain the quality of recommendations at a high leve for a longer time. We also consider the context of online social networks and tag recommendation. We propose an algorithm that takes account of the popularity of the tags and the opinions of the users' neighborhood. But, unlike common nearest neighbors' approaches, our algorithm doe not rely on a fixed number of neighbors when computing a recommendation. We use a heuristic that bounds the network traversai in a way that allows to faster compute the recommendations while preserving the quality of the recommendations. Finally, we propose a novel approach that improves the accuracy of the recommendations for top-k algorithms. Instead of a fixed list size, we adjust the number of items to recommend in a way that optimizes the likelihood that ail the recommended items will be chosen by the user, and find the best candidate sub-list to recommend to the user.
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Submitted on : Wednesday, January 5, 2022 - 1:37:07 PM
Last modification on : Thursday, January 6, 2022 - 3:43:41 AM
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  • HAL Id : tel-03512432, version 1



Modou Gueye. Managing recommendation data in large scale. Information Retrieval [cs.IR]. Télécom ParisTech, 2014. English. ⟨NNT : 2014ENST0083⟩. ⟨tel-03512432⟩



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