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Probabilistic graphic model is an elegant framework to compactly presentcomplex real-world observations by modeling uncertainty and logical flow(conditionally independent factors). In this paper, we present a probabilisticframework of neighborhood-based recommendation methods (PNBM) in whichsimilarity is regarded as an unobserved factor. Thus, PNBM leads the estimationof user preference to maximizing a posterior over similarity. We furtherintroduce a novel multi-layer similarity descriptor which models and learns thejoint influence of various features under PNBM, and name the new frameworkMPNBM. Empirical results on real-world datasets show that MPNBM allows veryaccurate estimation of user preferences.

A Probabilistic View of Neighborhood-based Recommendation Methods