Data Integration for Recommendation Systems

The quality of large-scale recommendation systems has been insufficient in terms of the accuracy of prediction. One of the major reasons is caused by the sparsity of the samples, usually represented by vectors of users' ratings on a set of items. Combining information other than users' ratings can provide the learning model complementary views of the data and, thus, a more accurate prediction. In this paper, we propose efficient methods for finding the best combination weights among single kernels. The weight parameters are optimized by aligning the combination kernel to ideal kernels. We solve the kernel alignment problem by linear programming techniques.

• Type: Conference
• Year: 2008
• Where: ICMLA
• Authors: Zhonghang Xia, Houduo Qi, Manghui Tu, Wenke Zhang
• Keywords: ICMLA 2008, Kernel Alignment Problem, Large-scale Recommendation Systems, Machine Learning, Model Complementary Views