The goal of the thesis is to extend the kernel methods to matrix factorization(MF) for collaborative filtering(CF). In current literature, MF methods of CF usually assume that the correlated data is distributed on a linear hyperplane, which is not always the case. Conventionally, the best known member of kernel methods is support vector machine (SVM) in terms of classification problems on linearly non-separable data. In this thesis, we apply kernel methods on MF, embedding the data into a possibly higher dimensional space and conduct factorization in that space. To improve kernelized matrix factorization, we apply multi-kernel learning methods to select optimal kernel functions from the candidates and introducing L2-norm regularization on the weight learning process. In our empirical study, we conduct experiments on three real-world datasets. The results suggest the proposed method improve the accuracy of the prediction that surpassing the results of multiple state-of-art CF methods.
My advisor is Prof. Xiangnan Kong, and Reader is Prof. Randy Paffenroth.