A Probabilistic View of Neighborhood-based Recommendation Methods
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.
Cite
@article{arxiv.1701.01250,
title = {A Probabilistic View of Neighborhood-based Recommendation Methods},
author = {Jun Wang and Qiang Tang},
journal= {arXiv preprint arXiv:1701.01250},
year = {2017}
}
Comments
accepted by: ICDM 2016 - IEEE International Conference on Data Mining series (ICDM) workshop CLOUDMINE, 7 pages