Semantic Shilling Attack against Heterogeneous Information Network Based Recommend Systems-Reference-Cited by-同舟云学术

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Semantic Shilling Attack against Heterogeneous Information Network Based Recommend Systems

Published:2022-02-01 Issue:2 Volume:E105.D Page:289-299
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

REN Yizhi¹,LI Zelong¹,YUAN Lifeng¹,ZHANG Zhen¹,SU Chunhua²,WANG Yujuan¹,WU Guohua¹

Affiliation:

1. School of Cyber Space Security, Hangzhou Dianzi University

2. Division of Computer Science, University of Aizu

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E105.D/2/E105.D_2021BCP0015/_pdf

Reference28 articles.

1. [1] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans. Knowl. Data Eng., vol.17, no.6, pp.734-749, 2005. 10.1109/tkde.2005.99

2. [2] S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM Computing Surveys (CSUR), vol.52, no.1, pp.1-38, 2019. 10.1145/3285029

3. [3] C. Shi, Z. Zhang, P. Luo, P.S. Yu, Y. Yue, and B. Wu, “Semantic path based personalized recommendation on weighted heterogeneous information networks,” Proc. 24th ACM International on Conference on Information and Knowledge Management, pp.453-462, 2015. 10.1145/2806416.2806528

4. [4] Z. Wang, H. Liu, Y. Du, Z. Wu, and X. Zhang, “Unified embedding model over heterogeneous information network for personalized recommendation,” IJCAI, pp.3813-3819, 2019. 10.24963/ijcai.2019/529

5. [5] S. Fan, J. Zhu, X. Han, C. Shi, L. Hu, B. Ma, and Y. Li, “Metapath-guided heterogeneous graph neural network for intent recommendation,” Proc. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.2478-2486, 2019. 10.1145/3292500.3330673

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