Titre :

Preference-like Score to Cope with Cold-Start User in Recommender Systems

Auteur(s) :

Felício, Crícia [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Paixão, Klérisson [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Barcelos, Celia [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Preux, Philippe [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]

Titre de la manifestation scientifique :

28th International Conference on Tools with Artificial Intelligence (ICTAI)

Ville :

San Jose

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2016-11-06

Titre de la revue :

Proceedings of the IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)

Date de publication :

2016-11-08

Mot(s)-clé(s) en anglais :

Cold-start User
Social Recommender
Recommendation system

Discipline(s) HAL :

Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Web

Résumé en anglais : [en]

—In recent years, there has been an explosion of social recommender systems (SRS) research. However, the dominant trend of these studies has been towards designing new prediction models. The typical approach is to use ...
Lire la suite >—In recent years, there has been an explosion of social recommender systems (SRS) research. However, the dominant trend of these studies has been towards designing new prediction models. The typical approach is to use social information to build those models for each new user. Due to the inherent complexity of this prediction process, for full cold-start user in particular, the performance of most SRS fall a great deal. We, rather, propose that new users are best served by models already built in system. Selecting a prediction model from a set of strong linked users might offer better results than building a personalized model for full cold-start users. We contribute to this line of work comparing several matrix factorization based SRS under full cold-start user scenario; and proposing a general model selection approach, called ToSocialRec, that leverages existing recommendation models to offer items for new users. Our framework is not only able to handle several social network connection weight metrics, but any metric that can be correlated with preference similarity among users, named here as Preference-like score. We perform experiments on real life datasets that show this technique is as efficient or more than current state-of-the-art techniques for cold-start user. Our framework has also been designed to be easily deployed and leveraged by developers to help create a new wave of SRS.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL