Identifier

etd-012516-110533

Abstract

The goal of the thesis is to extend the kernel methods to matrix factorization(MF) for collaborative ltering(CF). In current literature, MF methods usually assume that the correlated data is distributed on a linear hyperplane, which is not always the case. The best known member of kernel methods is support vector machine (SVM) 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 introduce L2-norm regularization on the weight learning process. In our empirical study, we conduct experiments on three real-world datasets. The results suggest that the proposed method can improve the accuracy of the prediction surpassing state-of-art CF methods.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Data Science

Project Type

Thesis

Date Accepted

2016-01-25

Accessibility

Unrestricted

Subjects

collaborative filtering, multiple kernel matrix factorization, L2-norm regularization

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