Shilling Attack Detection in Recommender Systems via Selecting Patterns Analysis-Reference-Cited by-同舟云学术

Shilling Attack Detection in Recommender Systems via Selecting Patterns Analysis

Published:2016 Issue:10 Volume:E99.D Page:2600-2611
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

LI Wentao¹,GAO Min¹²,LI Hua¹³,ZENG Jun¹²,XIONG Qingyu¹²,HIROKAWA Sachio⁴

Affiliation:

1. Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education

2. School of Software Engineering, Chongqing University

3. College of Computer Science, Chongqing University

4. Research Institute for Information Technology, Kyushu University

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/E99.D/10/E99.D_2015EDP7500/_pdf

Reference32 articles.

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2. [2] K. Truong, F. Ishikawa, and S. Honiden, “Improving accuracy of recommender system by item clustering,” IEICE Trans. Inf. & Syst., vol.90, no.9, pp.1363-1373, 2007.

3. [3] J. Wang and Y. Zhang, “Opportunity model for e-commerce recommendation: right product; right time,” Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp.303-312, ACM, 2013.

4. [4] K. Wei, J. Huang, and S. Fu, “A survey of e-commerce recommender systems,” Service Systems and Service Management, 2007 International Conference on, pp.1-5, IEEE, 2007.

5. [5] B. Bhasker, “Comparative study of collaborative filtering algorithms.,” Proc. Int. Conf. Knowledge Discovery and Information Retrieval, Barcelona, Spain, 2012, pp.132-137, 2012.

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