Symmetrization and overfitting in probabilistic latent semantic analysis

被引：1
|
作者：
Leksin V.A. [1 ]
机构：
[1] Moscow Institute of Physics and Technology, Dolgoprudnyi, Moscow oblast 141700
来源：
Pattern Recognition and Image Analysis | 2009年 / 19卷 / 04期
基金：
俄罗斯基础研究基金会;
关键词：
Collaborative filtering; Customer environment analysis; Latent profiles; Overfitting; Probabilistic latent semantic analysis; Symmetric models;
D O I：
10.1134/S1054661809040014
中图分类号：
学科分类号：
摘要：
An algorithm is proposed for revealing latent user's interests from the observable protocol of users behavior, e.g., site visits. The algorithm combines the ideas of customer environment analysis and probabilistic latent semantic analysis. A quality criterion based on the classification of preliminarily labeled sites is introduced to optimize the algorithm parameters and compare algorithms. The experiments show that the quality has an optimum by the essential parameters of the algorithm, however the attempt of too precise optimization can lead to overfitting. © 2009 Pleiades Publishing, Ltd.
引用
收藏
页码：565 / 574
页数：9
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