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arXiv · 1701.01250

A Probabilistic View of Neighborhood-based Recommendation Methods

Jun Wang·Qiang Tang
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Abstract

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.

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Jun Wang, Qiang Tang. 2017-01-05. A Probabilistic View of Neighborhood-based Recommendation Methods. https://arxiv.org/abs/1701.01250

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