Semi-Supervised Learning for Personalized Web Recommender System

Authors

  • Tingshao Zhu
  • Bin Hu
  • Jingzhi Yan
  • Xiaowei Li

Keywords:

Web behavioral modeling, data mining, computational cyberpsychology

Abstract

To learn a Web browsing behavior model, a large amount of labelled data must be available beforehand. However, very often the labelled data is limited and expensive to generate, since labelling typically requires human expertise. It could be even worse when we want to train personalized model. This paper proposes to train a personalized Web browsing behavior model by semi-supervised learning. The preliminary result based on the data from our user study shows that semi-supervised learning performs fairly well even though there are very few labelled data we can obtain from the specific user.

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Author Biographies

Tingshao Zhu

Graduate University of Chinese Academy of Sciences
Beijing, China

Bin Hu

School of Information Science and Engineering
Lanzhou University, Lanzhou, China

Jingzhi Yan

School of Information Science and Engineering
Lanzhou University, Lanzhou, China

Xiaowei Li

School of Information Science and Engineering
Lanzhou University, Lanzhou, China

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Published

2012-01-26

How to Cite

Zhu, T., Hu, B., Yan, J., & Li, X. (2012). Semi-Supervised Learning for Personalized Web Recommender System. Computing and Informatics, 29(4), 617–627. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/104

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