Support vector machines for collaborative filtering

Support Vector Machines (SVMs) have successfully shown efficiencies in many areas such as text categorization. Although recommendation systems share many similarities with text categorization, the performance of SVMs in recommendation systems is not acceptable due to the sparsity of the user-item matrix. In this paper, we propose a heuristic method to improve the predictive accuracy of SVMs by repeatedly correcting the missing values in the user-item matrix. The performance comparison to other algorithms has been conducted. The experimental studies show that the accurate rates of our heuristic method are the highest. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms, Measurement, Experimentation Keywords Recommendation systems, Support Vector Machines, Machine Learning, Collaborative Filtering

• Type: Conference
• Year: 2006
• Where: ACMSE
• Authors: Zhonghang Xia, Yulin Dong, Guangming Xing
• Keywords: ACMSE 2006, Recommendation Systems, Support Vector Machines, Text Categorization, Theoretical Computer Science