Large-scale Bandit Recommender System
Document type :
Communication dans un congrès avec actes
Title :
Large-scale Bandit Recommender System
Author(s) :
Guillou, Frédéric [Auteur]
Sequential Learning [SEQUEL]
Gaudel, Romaric [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Preux, Philippe [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Sequential Learning [SEQUEL]
Gaudel, Romaric [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Preux, Philippe [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Scientific editor(s) :
Pardalos, Panos M.
Conca, Piero
Giuffrida, Giovanni
Nicosia, Giuseppe
Conca, Piero
Giuffrida, Giovanni
Nicosia, Giuseppe
Conference title :
Proc. of the Second International Workshop on Machine Learning, Optimization and Big Data (MOD)
City :
Volterra
Country :
Italie
Start date of the conference :
2016-09-14
Journal title :
Lecture Notes in Computer Science
Publisher :
Springer International Publishing
Publication date :
2016
HAL domain(s) :
Informatique [cs]/Apprentissage [cs.LG]
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
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