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.
[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
