Semi-supervised Collaborative Text Classification

Most text categorization methods require text content of documents that is often difficult to obtain. We consider "Collaborative Text Categorization", where each document is represented by the feedback from a large number of users. Our study focuses on the semisupervised case in which one key challenge is that a significant number of users have not rated any labeled document. To address this problem, we examine several semi-supervised learning methods and our empirical study shows that collaborative text categorization is more effective than content-based text categorization and the manifold regularization is more effective than other state-of-the-art semi-supervised learning methods.

• Type: Conference
• Year: 2007
• Where: ECML
• Authors: Rong Jin, Ming Wu, Rahul Sukthankar
• Keywords: Collaborative Text Categorization, ECML 2007, Machine Learning, Text Categorization, Text Categorization Methods