NDLI: Detecting abnormal profiles in collaborative filtering recommender systems

Content Provider	Springer Nature Link
Author	Yang, Zhihai Cai, Zhongmin
Copyright Year	2016
Abstract	Personalization collaborative filtering recommender systems (CFRSs) are the crucial components of popular E-commerce services. In practice, CFRSs are also particularly vulnerable to “shilling” attacks or “profile injection” attacks due to their openness. The attackers can inject well-designed attack profiles into CFRSs in order to bias the recommendation results to their benefits. To reduce this risk, various detection techniques have been proposed to detect such attacks, which use diverse features extracted from user profiles. However, relying on limited features to improve the detection performance is difficult seemingly, since the existing features can not fully characterize the attack profiles and genuine profiles. In this paper, we propose a novel detection method to make recommender systems resistant to such attacks. The existing features can be briefly summarized as two aspects including rating behavior based and item distribution based. We firstly formulate the problem as finding a mapping model between rating behavior and item distribution by exploiting the least-squares approximate solution. Based on the trained model, we design a detector by employing a regressor to detect such attacks. Extensive experiments on both the MovieLens-100K and MovieLens-ml-latest-small datasets examine the effectiveness of the proposed detection method. Experimental results demonstrate the outperformance of the proposed approach in comparison with benchmarked method including KNN.
Starting Page	499
Ending Page	518
Page Count	20
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	48
Issue Number	3
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2016-07-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Recommender system Shilling attack Attack detection Information Storage and Retrieval Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) IT in Business Document Preparation and Text Processing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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