A Multi-Armed Bandit Model Selection for ...
Type de document :
Communication dans un congrès avec actes
Titre :
A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation
Auteur(s) :
Felício, Crícia [Auteur]
Paixão, Klérisson [Auteur]
Barcelos, Celia [Auteur]
Preux, Philippe [Auteur]
Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Paixão, Klérisson [Auteur]
Barcelos, Celia [Auteur]
Preux, Philippe [Auteur]
Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
25th ACM Conference on User Modelling, Adaptation and Personalization (UMAP)
Ville :
Bratislava
Pays :
Slovaquie
Date de début de la manifestation scientifique :
2017-07-09
Date de publication :
2017-07-01
Mot(s)-clé(s) en anglais :
Recommender system; Cold-start problem; Model selection; Multi- armed bandits
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
How can we effectively recommend items to a user about whom we have no information? This is the problem we focus on, known as the cold-start problem. In this paper, we focus on the cold user problem.In most existing works, ...
Lire la suite >How can we effectively recommend items to a user about whom we have no information? This is the problem we focus on, known as the cold-start problem. In this paper, we focus on the cold user problem.In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information?Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users yield uncertain returns. Assuming a number of alternative prediction models is available to select items to recommend to a cold user, this paper introduces a multi-armed bandit based model selection, named PdMS.In comparison with two baselines, PdMS improves the performance as measured by the nDCG.These improvements are demonstrated on real, public datasets.Lire moins >
Lire la suite >How can we effectively recommend items to a user about whom we have no information? This is the problem we focus on, known as the cold-start problem. In this paper, we focus on the cold user problem.In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information?Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users yield uncertain returns. Assuming a number of alternative prediction models is available to select items to recommend to a cold user, this paper introduces a multi-armed bandit based model selection, named PdMS.In comparison with two baselines, PdMS improves the performance as measured by the nDCG.These improvements are demonstrated on real, public datasets.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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