Collaborative Filtering as a Multi-Armed Bandit

Guillou, Frédéric; Gaudel, Romaric; Preux, Philippe

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

Title :

Collaborative Filtering as a Multi-Armed Bandit

Author(s) :

Guillou, Frédéric [Auteur correspondant]
Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Gaudel, Romaric [Auteur]
Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Preux, Philippe [Auteur]

Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Conference title :

NIPS'15 Workshop: Machine Learning for eCommerce

City :

Montréal

Country :

Canada

Start date of the conference :

2015-12-11

English keyword(s) :

Recommender Systems
sequential Recommender Systems
Collaborative Filtering
Matrix Factorization
Multi-Armed Bandtis

HAL domain(s) :

Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

Recommender Systems (RS) aim at suggesting to users one or several items in which they might have interest. Following the feedback they receive from the user, these systems have to adapt their model in order to improve ...
Show more >Recommender Systems (RS) aim at suggesting to users one or several items in which they might have interest. Following the feedback they receive from the user, these systems have to adapt their model in order to improve future recommendations. The repetition of these steps defines the RS as a sequential process. This sequential aspect raises an exploration-exploitation dilemma, which is surprisingly rarely taken into account for RS without contextual information. In this paper we present an explore-exploit collaborative filtering RS, based on Matrix Factor-ization and Bandits algorithms. Using experiments on artificial and real datasets, we show the importance and practicability of using sequential approaches to perform recommendation. We also study the impact of the model update on both the quality and the computation time of the recommendation procedure.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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Collaborative Filtering as a Multi-Armed Bandit

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