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 userspsila ratings on a set of items. Combining information other than userspsila 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.

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