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Shilling attacks against collaborative recommender systems: a review

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Abstract

Collaborative filtering recommender systems (CFRSs) have already been proved effective to cope with the information overload problem since they merged in the past two decades. However, CFRSs are highly vulnerable to shilling or profile injection attacks since their openness. Ratings injected by malicious users seriously affect the authenticity of the recommendations as well as users’ trustiness in the recommendation systems. In the past two decades, various studies have been conducted to scrutinize different profile injection attack strategies, shilling attack detection schemes, robust recommendation algorithms, and to evaluate them with respect to accuracy and robustness. Due to their popularity and importance, we survey about shilling attacks in CFRSs. We first briefly discuss the related survey papers about shilling attacks and analyze their deficiencies to illustrate the necessity of this paper. Next we give an overall picture about various shilling attack types and their deployment modes. Then we explain profile injection attack strategies, shilling attack detection schemes and robust recommendation algorithms proposed so far in detail. Moreover, we briefly explain evaluation metrics of the proposed schemes. Last, we discuss some research directions to improve shilling attack detection rates robustness of collaborative recommendation, and conclude this paper.

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Notes

  1. http://news.bbc.co.uk/2/hi/entertainment/1368666.stm.

  2. https://www.cnet.com/news/amazon-blushes-over-sex-link-gaffe/.

  3. http://www.auctionbytes.com/cab/abn/y03/m09/i17/s01.

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Acknowledgements

This work is supported by the Projects (61672401, 61373045) supported by the National Natural Science Foundation of China; Project (315***10101) supported by the Pre-Research Project of the “Thirteenth Five-Year-Plan” of China; Projects (JBG161002) supported by the Fundamental Research Funds for the Central Universities of China.

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  1. School of Computer Science and Technology, Xidian University, Xi’an, 710000, China

    Mingdan Si & Qingshan Li

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  1. Mingdan Si
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  2. Qingshan Li
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Correspondence to Qingshan Li.

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Si, M., Li, Q. Shilling attacks against collaborative recommender systems: a review. Artif Intell Rev 53, 291–319 (2020). https://doi.org/10.1007/s10462-018-9655-x

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