Vishal Gupta, Saakshi Saakshi Kapoor, Rohit Kumar


Today, there is lots of information available over the Internet but it’s very difficult to filter out the required information from this overload of information. Thus a solution to this problem, came as “Recommender Systems”, they can predict outcomes according to user’s interests. Although Recommender Systems are very effective and useful for users but the mostly used type of Recommender System i.e. Collaborative Filtering Recommender System suffers from shilling/profile injection attacks in which fake profiles are inserted into the database in order to bias its output. This paper is aimed at discussing various attacks that can affect Recommender Systems and the attributes that are used for the detection of these attacks.


Recommender Systems, Shilling attacks; Generic attributes; Model specific attributes

Full Text:



Prem Melville and VikasSindhwani. "Recommender systems." In Encyclopedia of machine learning, pp. 829-838. Springer US, 2011.

Robin Burke,"Hybrid recommender systems: Survey and experiments." User modeling and user-adapted interaction 12, no. 4 (2002): 331-370.

BamshadMobasher, Robin Burke, RunaBhaumik, and Jeff J. Sandvig. "Attacks and remedies in collaborative recommendation." IEEE Intelligent Systems 22, no. 3 (2007).

F. O. Isinkaye, Y. O. Folajimi, and B. A. Ojokoh. "Recommendation systems: Principles, methods and evaluation." Egyptian Informatics Journal 16, no. 3 (2015): 261-273.

Zhihai Yang, and ZhongminCai. "Detecting abnormal profiles in collaborative filtering recommender systems." Journal of Intelligent Information Systems (2016): 1-20.

Quanqiang Zhou and Fuzhi Zhang. "A Hybrid Unsupervised Approach for Detecting Profile Injection Attacks in Collaborative Recommender Systems." Journal of Information &Computational Science 9, no. 3 (2012): 687-694.

ZhuoZhang, and Sanjeev R. Kulkarni. "Detection of shilling attacks in recommender systems via spectral clustering." In Information Fusion (FUSION), 2014 17th International Conference on Information Fusion, pp. 1-8. IEEE, 2014.

Chad Williams,RunaBhaumik, J. J. Sandvig, BamshadMobasher, and Robin Burke. “Evaluation of profile injection attacks in collaborative recommender systems.” Technical report. Available from http://facweb. cti. depaul. edu/research/techreports/TR06-006. pdf.(accessed 17.03. 12), 2008.

Wei Zhou, Junhao Wen, Yun Sing Koh, ShafiqAlam, and Gillian Dobbie. "Attack detection in recommender systems based on target item analysis." In Neural Networks (IJCNN), 2014 International Joint Conference on Neural Networks, pp. 332-339. IEEE, 2014.

QuanqiangZhou. "Supervised approach for detecting average over popular items attack in collaborative recommender systems." IET Information Security 10, no. 3 (2016): 134-141.

WilanderBhebe, and Okuthe P. Kogeda. "Shilling attack detection in Collaborative Recommender Systems using a Meta Learning strategy." In Emerging Trends in Networks and Computer Communications (ETNCC), 2015 International Conference on Emerging Trends in Networks and Computer Communications, pp. 56-61. IEEE, 2015.

GediminasAdomaviciusand Alexander Tuzhilin. "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions." IEEE transactions on knowledge and data engineering 17, no. 6 (2005): 734-749.

DhohaAlmazro,GhadeerShahatah, LamiaAlbdulkarim, Mona Kherees, Romy Martinez, and William Nzoukou. "A survey paper on recommender systems." arXiv preprint arXiv: 1006.5278 (2010).

XiaoyuanSu, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009)

RunaBhaumik, BamshadMobasher, and Robin Burke. "A clustering approach to unsupervised attack detection in collaborative recommender systems." In Proceedings of the 7th IEEE international conference on data mining, Las Vegas, NV, USA, pp. 181-187. 2011.

FuguoZhang. "A survey of shilling attacks in collaborative filtering recommender systems." In Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference onComputational Intelligence and Software Engineering, pp. 1-4. IEEE, 2009.

QiangZhang, Yuan Luo, ChuliangWeng, and Minglu Li. "A trust-based detecting mechanism against profile injection attacks in recommender systems." In Secure Software Integration and Reliability Improvement, 2009. SSIRI 2009. Third IEEE International Conference onSecure Software Integration and Reliability Improvement, pp. 59-64. IEEE, 2009.

Mohammad Amin Morid, Mehdi Shajari, and Ali Reza Hashemi. "Defending recommender systems by influence analysis." Information retrieval 17, no. 2 (2014): 137-152.

IhsanGunes, CihanKaleli, Alper Bilge, and HuseyinPolat. "Shilling attacks against recommender systems: a comprehensive survey." Artificial Intelligence Review (2014): 1-33.

Chad A.Williams, BamshadMobasher, and Robin Burke. "Defending recommender systems: detection of profile injection attacks." Service Oriented Computing and Applications 1, no. 3 (2007): 157-170.

DOI: https://doi.org/10.26483/ijarcs.v8i7.4550


  • There are currently no refbacks.

Copyright (c) 2017 International Journal of Advanced Research in Computer Science