Collaborative filtering via gaussian probabilistic latent semantic analysis

Thomas Hofmann
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引用次数: 449

Abstract

Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic latent semantic analysis to continuous-valued response variables. More specifically, we assume that the observed user ratings can be modeled as a mixture of user communities or interest groups, where users may participate probabilistically in one or more groups. Each community is characterized by a Gaussian distribution on the normalized ratings for each item. The normalization of ratings is performed in a user-specific manner to account for variations in absolute shift and variance of ratings. Experiments on the EachMovie data set show that the proposed approach compares favorably with other collaborative filtering techniques.
基于高斯概率潜在语义分析的协同过滤
协同过滤旨在从社区数据中学习用户偏好、兴趣或行为的预测模型,即可用用户偏好的数据库。在本文中,我们描述了一种新的基于模型的算法,该算法基于对连续值响应变量的概率潜在语义分析的推广。更具体地说,我们假设观察到的用户评级可以建模为用户社区或兴趣组的混合,其中用户可能概率地参与一个或多个组。每个社区的特征是每个项目的标准化评级的高斯分布。评级的规范化以特定于用户的方式执行,以解释评级的绝对移位和方差的变化。在EachMovie数据集上的实验表明,该方法优于其他协同过滤技术。
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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