Title :
Kernel-based Approaches for Collaborative Filtering
Author :
Xia, Zhonghang ; Zhang, Wenke ; Tu, Manghui ; Yen, I-Ling
Author_Institution :
Dept. of Math & Comput. Sci., Western Kentucky Univ., Bowling Green, KY, USA
Abstract :
In a large-scale collaborative filtering system, pair wise similarity between users is usually measured by users´ ratings on the whole set of items. However, this measurement may not be well defined due to the sparsity problem, i.e., the lack of adequate ratings on items for calculating accurate predictions. In fact, most correlated users have similar ratings only on a subset of items. In this paper, we consider a kernel-based classification approach for collaborative filtering and propose several kernel matrix construction methods by using biclusters to capture pair wise similarity between users. In order to characterize accurate correlation among users, we embed both local information and global information into the similarity matrix. However, this similarity matrix may not be a kernel matrix. Our solution is to approximate it with the matrix close to it and use low rank constraints to control the complexity of the matrix.
Keywords :
groupware; information filtering; pattern classification; adequate ratings; biclusters; global information; kernel based approach; kernel based classification; kernel matrix construction; large-scale collaborative filtering system; pair wise similarity; pairwise similarity; similarity matrix; sparsity problem; Accuracy; Collaboration; Data models; Kernel; Machine learning; Support vector machines; Training; bicluster; collaborative filtering; kernel method;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-9211-4
DOI :
10.1109/ICMLA.2010.41