Affiliation:
1. Hunan University, Changsha City, Hunan Province, China
2. Hunan University and State University of New York, New York, USA
Abstract
CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.
Funder
NSFC
Guangdong Provincial NSF
Open Project of Zhejiang Lab
China Scholarships Council
Science and Technology Program of Changsha City
NSF
National Outstanding Youth Science Program of NSFC
Publisher
Association for Computing Machinery (ACM)
Reference61 articles.
1. 2018. Behance.net social network. Retrieved from http://www.behance.net/. 2018. Behance.net social network. Retrieved from http://www.behance.net/.
2. Predicting the popularity of online content with group-specific models;Cao Qi;Communications of the ACM,2017
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献