Reference : A Probabilistic View of Neighborhood-based Recommendation Methods
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/29142
A Probabilistic View of Neighborhood-based Recommendation Methods
English
Wang, Jun mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Tang, Qiang mailto []
12-Dec-2016
ICDM 2016 - IEEE International Conference on Data Mining series (ICDM) workshop CLOUDMINE
Yes
International
ICDM 2016 - IEEE International Conference on Data Mining series (ICDM) workshop CLOUDMINE
from 12-12-2016 to 15-12-2016
[en] recommender system ; probabilistic graphical model ; neighborhood
[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.
FNR
http://hdl.handle.net/10993/29142
FnR ; FNR5856658 > Qiang Tang > BRAIDS > Boosting Security and Efficiency in Recommender Systems > 15/04/2014 > 14/04/2017 > 2013

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