Title :

Exploiting Social Information in Pairwise Preference Recommender System

Author(s) :

Felício, Crícia [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Paixão, Klérisson [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Alves, Guilherme [Auteur]
Universidade Federal de Lavras = Federal University of Lavras [UFLA]
Federal University of Uberlândia [Uberlândia] [UFU]
de Amo, Sandra [Auteur]
Faculdade de Computação
Preux, Philippe [Auteur]
Sequential Learning [SEQUEL]

Journal title :

Journal of Information and Data Management

Pages :

16

Publisher :

Brazilian Computer Society

Publication date :

2016-08-15

English keyword(s) :

H33 [Information Storage and Retrieval]: Clustering-Information filtering
Social Recommender System

HAL domain(s) :

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

English abstract : [en]

There has been an explosion of social approaches to leverage recommender systems, mainly to deal with cold-start problems. However, most of the approaches are designed to handle explicit user's ratings. We have envisioned ...
Show more >There has been an explosion of social approaches to leverage recommender systems, mainly to deal with cold-start problems. However, most of the approaches are designed to handle explicit user's ratings. We have envisioned Social PrefRec, a social recommender that applies user preference mining and clustering techniques to incorporate social information on the pairwise preference recommenders. Our approach relies on the hypothesis that user's preference is similar to or influenced by their connected friends. This study reports experiments evaluating the recommendation quality of this method to handle the cold-start problem. Moreover, we investigate the effects of several social metrics on pairwise preference recommendations. We also show the effectiveness of our social preference learning approach in contrast to state-of-the-art social recommenders, expanding our understanding of how contextual social information affects pairwise recommenders.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