Back to Search
Start Over
Shilling attack detection for recommender systems based on credibility of group users and rating time series
- Source :
- PLoS ONE, Vol 13, Iss 5, p e0196533 (2018), PLoS ONE
- Publication Year :
- 2018
- Publisher :
- Public Library of Science (PLoS), 2018.
-
Abstract
- Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.
- Subjects :
- Computer science
Economics
Social Sciences
lcsh:Medicine
02 engineering and technology
computer.software_genre
Machine Learning
Mathematical and Statistical Techniques
Sociology
Credibility
0202 electrical engineering, electronic engineering, information engineering
Medicine and Health Sciences
Public and Occupational Health
Computer Networks
lcsh:Science
Nuclear Weapons
Multidisciplinary
User profile
Item analysis
Data stream mining
Applied Mathematics
Simulation and Modeling
Traumatic Injury Risk Factors
Physical Sciences
Engineering and Technology
020201 artificial intelligence & image processing
The Internet
Data mining
Weapons
Algorithms
Statistics (Mathematics)
Research Article
Employment
Computer and Information Sciences
Maintenance
Equipment
Recommender system
Research and Analysis Methods
Text mining
Artificial Intelligence
020204 information systems
Mass Media
Statistical Methods
Time Series Analysis
Computer Security
Internet
business.industry
lcsh:R
Consumer Behavior
Communications
Labor Economics
lcsh:Q
business
computer
Software
Mathematics
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....701d97e808285f288a74918d0bf2c153