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General Information
    • ISSN: 1793-8244
    • Frequency: Semiyearly
    • DOI: 10.18178/JACN
    • Editor-in-Chief: Dr. Ka Wai Gary Wong
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), Engineering & Technology Digital Library, DOAJ, Electronic Journals Library, Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest, and Google Scholar.
    • E-mail: jacn@ejournal.net
Editor-in-chief
Dr. Ka Wai Gary Wong
Division of Information and Technology Studies, Faculty of Education, The University of Hong Kong.
It's a honor to serve as the editor-in-chief of JACN. I'll work together with the editors and reviewers to help the journal progress
JACN 2016 Vol.4(1): 64-69 ISSN: 1793-8244
DOI: 10.18178/JACN.2016.4.1.205

Weighted Bipartite Network Projection for Personalized Recommendations

Jing Wang, Fengjing Shao, Shunyao Wu, Rencheng Sun, and Ran Li
Abstract—To reduce the difficulty of personalized recommendations, the traditional network-based method constructed bipartite networks with stronger links (higher ratings). However, weaker links and link weights were almost ignored. Although the existing method effectively mined users’ preferences, it was impossible to catch users’ disgusts. Therefore, this paper proposed a novel method to effectively discover users’ preferences and disgusts. Experimental results on the MovieLens dataset demonstrated that the proposed method was much more superior to the baseline method under the diversity index.

Index Terms—Personalized recommendations, weighted bipartite network, users’ preferences, users’ disgusts, diversity.

Jing Wang, Fengjing Shao, Shunyao Wu, Rencheng Sun, and Ran Li are with Qingdao University, Shandong, China (e-mail: sfj@qdu.edu.cn).

[PDF]

Cite:Jing Wang, Fengjing Shao, Shunyao Wu, Rencheng Sun, and Ran Li, "Weighted Bipartite Network Projection for Personalized Recommendations," Journal of Advances in Computer Networks vol. 4, no. 1, pp. 64-69, 2016.

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