Large-scale Bandit Recommender System

Frédéric Guillou 1 Romaric Gaudel 1, 2 Philippe Preux 1, 2
1 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : The main target of Recommender Systems (RS) is to propose to users one or several items in which they might be interested. However, as users provide more feedback, the recommendation process has to take these new data into consideration. The necessity of this update phase makes recommendation an intrinsically sequential task. A few approaches were recently proposed to address this issue, but they do not meet the need to scale up to real life applications. In this paper , we present a Collaborative Filtering RS method based on Matrix Factorization and Multi-Armed Bandits. This approach aims at good recommendations with a narrow computation time. Several experiments on large datasets show that the proposed approach performs personalized recommendations in less than a millisecond per recommendation.
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Pardalos, Panos M.; Conca, Piero; Giuffrida, Giovanni; Nicosia, Giuseppe. Proc. of the Second International Workshop on Machine Learning, Optimization and Big Data (MOD), Sep 2016, Volterra, Italy. Springer International Publishing, 10122, pp.11, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-51469-7_17〉
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Frédéric Guillou, Romaric Gaudel, Philippe Preux. Large-scale Bandit Recommender System. Pardalos, Panos M.; Conca, Piero; Giuffrida, Giovanni; Nicosia, Giuseppe. Proc. of the Second International Workshop on Machine Learning, Optimization and Big Data (MOD), Sep 2016, Volterra, Italy. Springer International Publishing, 10122, pp.11, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-51469-7_17〉. 〈hal-01406389〉

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