[en] Probabilistic graphic model is an elegant framework to compactly present complex real-world observations by modeling uncertainty and logical flow (conditionally independent factors). In this paper, we present a probabilistic framework of neighborhood-based recommendation methods (PNBM) in which similarity is regarded as an unobserved factor. Thus, PNBM leads the estimation of user preference to maximizing a posterior over similarity. We further introduce a novel multi-layer similarity descriptor which models and learns the joint influence of various features under PNBM, and name the new framework MPNBM. Empirical results on real-world datasets show that MPNBM allows very accurate estimation of user preferences.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
WANG, Jun ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Tang, Qiang
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
A Probabilistic View of Neighborhood-based Recommendation Methods
Date de publication/diffusion :
12 décembre 2016
Nom de la manifestation :
ICDM 2016 - IEEE International Conference on Data Mining series (ICDM) workshop CLOUDMINE
Date de la manifestation :
from 12-12-2016 to 15-12-2016
Manifestation à portée :
International
Titre de l'ouvrage principal :
ICDM 2016 - IEEE International Conference on Data Mining series (ICDM) workshop CLOUDMINE
Peer reviewed :
Peer reviewed
Projet FnR :
FNR5856658 - Boosting Security And Efficiency In Recommender Systems, 2013 (15/04/2014-14/04/2017) - Qiang Tang
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