Cascaded Trust Network-based Block-Incremental Recommendation Strategy

doi:10.23940/ijpe.19.03.p3.743755

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (3): 743-755.doi: 10.23940/ijpe.19.03.p3.743755

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Cascaded Trust Network-based Block-Incremental Recommendation Strategy

Shujuan Ji^{a, *}, Da Li^b, Qing Zhang^b, Chunjin Zhang^c, and Chunxiao Bao^b

a Key Laboratory for Wisdom Mine Information Technology of Shandong Province Shandong University of Science and Technology, Qingdao, 266590, China;
b College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China;
c Network Information Center (NIC), Shandong University of Science and Technology, Qingdao, 266590, China

Submitted on ; Revised on ;
Contact: jane_ji2003@aliyun.com
About author:Shujuan Ji is an associate professor in the College of Computer Science and Engineering at Shandong University of Science and Technology. She received her B.S., M.S., and Ph.D. degrees from Shandong University of Science and Technology. Her main research interests include artificial intelligence and intelligent business information processing.Da Li received his bachelor's degree from Qingdao University of Science and Technology. He is currently a Master's candidate at Shandong University of Science and Technology. His main research interests include artificial intelligence and intelligent business information processing.Qing Zhang received her B.S. and M.S. degrees in computer science and engineering from Shandong University of Science and Technology. Her research interests include artificial intelligence and intelligent business information processing.Chunjin Zhang is an engineer in the Center of Modern Education at Shandong University of Science and Technology. He received his B.S. degree in computer science and technology from the College of Information Science and Engineering at Shandong University of Science and Technology and his M.S. degree in control theory and control engineering from the College of Information and Electrical Engineering at Shandong University of Science and Technology.Chunxiao Bao is a Master's student in the College of Computer Science and Engineering at Shandong University of Science and Technology. Her research interests include game theory and intelligent business information processing.

Abstract

Abstract: Accurate recommendation can effectively bridge sellers and buyers. Because the computation complexity and storage complexity of static data-oriented recommendation algorithms are very high, researchers have recently explored streaming recommendation systems. However, streaming recommendation wastes large quantities of computation resources in quick response and is not suitable for seasonable-dependent situations. Therefore, this paper presents a block incremental recommendation strategy. First, a cascaded trust network construction method is presented, which is realized by using a distrust relationship to purify and predict users' trust relationships. Then, the social regularization is improved by comprehensively considering the cascaded trust relationship, the behavior bias of users and items. Finally, a block-incremental recommendation algorithm called ITDBMF is proposed, which uses the Ebbinghaus forgetting function to decay incremental rating blocks and simultaneously considers incremental social relationships. Experimental results show that the incremental recommendation strategy given in this paper can not only outperform benchmark algorithms in prediction accuracy, but also save storage of remote data and matrix factorization time.

Key words: personalized recommendation, matrix factorization, social network, trust cascaded network, block incremental, time decay

Shujuan Ji, Da Li, Qing Zhang, Chunjin Zhang, and Chunxiao Bao. Cascaded Trust Network-based Block-Incremental Recommendation Strategy [J]. Int J Performability Eng, 2019, 15(3): 743-755.

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Cascaded Trust Network-based Block-Incremental Recommendation Strategy

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