A Probabilistic View of Neighborhood-Based Recommendation Methods

Jun Wang, Qiang Tang
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引用次数: 5

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.
基于邻域的推荐方法的概率观点
概率图形模型是一个优雅的框架,通过建模不确定性和逻辑流(条件独立因素)紧凑地呈现复杂的现实世界的观察。在本文中,我们提出了一种基于邻域的推荐方法(PNBM)的概率框架,其中相似性被视为一个不可观察的因素。因此,PNBM导致用户偏好的估计最大化后验相似性。我们进一步引入了一种新的多层相似描述符,在PNBM下对各种特征的联合影响进行建模和学习,并将新框架命名为MPNBM。现实世界数据集的实证结果表明,MPNBM可以非常准确地估计用户偏好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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