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A survey of attack detection approaches in collaborative filtering recommender systems

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Abstract

Nowadays, due to the increasing amount of data, the use of recommender systems has increased. Therefore, the quality of the recommendations for the users of these systems is very important. One of the recommender systems models is collaborative filtering (CF) which uses the ratings given by the users to the items. But many of these ratings may be noisy or inaccurate so they reduce the quality of the recommendations. Sometimes users, using fake profiles, try to change the recommendations in their favor. Since satisfaction and trust in such systems are very important and useful, it would be better to find a way to identify these types of users. Despite numerous studies on CF recommender systems, the design of a robust recommender system is still a challenging problem. In this paper, we have analyzed the 25 previous samples of research on collaborative filtering recommender system (CFRS) for attack detection from 2009 to 2019. Most of these papers focus mainly on movie recommendations. According to these analyzes, we have categorized attack detection methods on CFRS in four categories: clustering, classifying, feature extraction and probabilistic approaches. The evaluation measures, the dataset, and attacks features used in the attack detection approaches are discussed.

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Notes

  1. Artificial neural network.

  2. Support vector machines-target item analysis.

  3. Synthetic minority over-sampling technique.

  4. Rating deviation from mean agreement.

  5. Mean absolute error.

  6. Principal component analysis.

  7. High rating ratio.

  8. Receiver operating characteristic.

  9. DEgsime’ and Rdma target item analysis.

  10. Partition around medoid.

  11. Absolute difference between medoid points.

  12. Angle-based outlier detection.

  13. Classification-based approach.

  14. Multi-dimensional scaling.

  15. Group rating deviation from mean agreement.

  16. Value based neighbor selection.

  17. Mean similarity-based expected profit.

  18. Random filling method.

  19. Average filling method.

  20. Hybrid perspective recommender system.

  21. Shilling attack detection.

  22. Mean of total profit.

  23. Mean similarity-based expected profit.

  24. Unsupervised retrieval of attack profiles.

  25. Hilbert–Huang transform.

  26. Rdma and Degsim-Target Item Analysis.

  27. Area under curve.

  28. Weighted deviation from mean agreement.

  29. Length variance.

  30. Discrete wavelet transform support vector machine.

  31. Convolutional neural network-Shilling Attack Detection.

  32. Probabilistic latent semantics analysis.

  33. Semi-supervised attack detection.

  34. Expectation–maximization.

  35. Rating deviation from mean bias.

  36. Compromised item deviation analysis.

  37. http://movielens.org.

  38. https://grouplens.org/datasets/jester/.

  39. https://grouplens.org/datasets/eachmovie/.

  40. http://jmcauley.ucsd.edu/data/amazon/.

  41. http://academictorrents.com/details/9b13183dc4d60676b773c9e2cd6de5e5542cee9a.

  42. http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

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Authors and Affiliations

  1. Computer Engineering Faculty, K.N.Toosi University of Technology, Tehran, Iran

    Fatemeh Rezaimehr & Chitra Dadkhah

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  1. Fatemeh Rezaimehr
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  2. Chitra Dadkhah
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Correspondence to Chitra Dadkhah.

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Rezaimehr, F., Dadkhah, C. A survey of attack detection approaches in collaborative filtering recommender systems. Artif Intell Rev 54, 2011–2066 (2021). https://doi.org/10.1007/s10462-020-09898-3

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