Probabilistic factor models for web site recommendation

Hao Ma, Chao Liu, Irwin King, Michael R. Lyu
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引用次数: 80

Abstract

Due to the prevalence of personalization and information filtering applications, modeling users' interests on the Web has become increasingly important during the past few years. In this paper, aiming at providing accurate personalized Web site recommendations for Web users, we propose a novel probabilistic factor model based on dimensionality reduction techniques. We also extend the proposed method to collective probabilistic factor modeling, which further improves model performance by incorporating heterogeneous data sources. The proposed method is general, and can be applied to not only Web site recommendations, but also a wide range of Web applications, including behavioral targeting, sponsored search, etc. The experimental analysis on Web site recommendation shows that our method outperforms other traditional recommendation approaches. Moreover, the complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations.
网站推荐的概率因子模型
由于个性化和信息过滤应用的流行,在Web上对用户的兴趣进行建模在过去几年中变得越来越重要。为了向Web用户提供准确的个性化网站推荐,本文提出了一种基于降维技术的概率因子模型。我们还将所提出的方法扩展到集体概率因子建模,通过整合异构数据源进一步提高了模型的性能。所提出的方法具有通用性,不仅可以应用于网站推荐,还可以应用于广泛的Web应用,包括行为定位、赞助搜索等。对网站推荐的实验分析表明,该方法优于其他传统的推荐方法。此外,复杂性分析表明,我们的方法可以应用于非常大的数据集,因为它随观测值的数量线性扩展。
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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