Collaborative Filtering as a Multi-Armed Bandit - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

Collaborative Filtering as a Multi-Armed Bandit


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.
Fichier principal
Vignette du fichier
main.pdf (374.23 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01256254 , version 1 (14-01-2016)


  • HAL Id : hal-01256254 , version 1


Frédéric Guillou, Romaric Gaudel, Philippe Preux. Collaborative Filtering as a Multi-Armed Bandit. NIPS'15 Workshop: Machine Learning for eCommerce, Dec 2015, Montréal, Canada. ⟨hal-01256254⟩
446 View
1828 Download


Gmail Facebook X LinkedIn More