  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.
