Support vector machines for collaborative filtering

@inproceedings{Xia2006SupportVM,
  title={Support vector machines for collaborative filtering},
  author={Zhonghang Xia and Yulin Dong and Guangming Xing},
  booktitle={ACM Southeast Regional Conference},
  year={2006}
}
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… CONTINUE READING
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