Abnormal Group User Detection in Recommender Systems Using Multi-dimension Time Series

  • Wei ZhouEmail author
  • Junhao Wen
  • Qingyu Xiong
  • Jun Zeng
  • Ling Liu
  • Haini Cai
  • Tian Chen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)


Collaborative filtering based recommender systems are capable of generating personalized recommendations, which are tools to alleviate information overload problem. However, due to the open nature of recommender systems, they are vulnerable to shilling attacks which insert forged user profiles to alter the recommendation list of targeted items. Previous research related to robustness of recommender systems has focused on detecting malicious profiles. Most approaches focus on profile classification but ignore the group attributes among shilling attack profiles. A method for detecting suspicious ratings by constructing multi-dimension time series TS-TIA is proposed. We reorganize all ratings on each item sorted by time series, each time series is examined and suspicious rating segments are checked. Then statistical metrics and target item analysis techniques are used to detect shilling attacks in these anomaly rating segments. Experiments show that our proposed method can be effective and less time consuming at detecting items under attacks in greater datasets.


Abnormal group users Shilling attack detection Time series Recommender system 



This research is supported by NSFC under grant No. 61602070, 61502062, 61379158 and China Postdoctoral Science Foundation under Grant No. 2014M560704.


  1. 1.
    Ma, Y., Wang, S., Yang, F., Chang, R.N.: Predicting QoS values via multi-dimensional QoS data for web service recommendations. In: 2015 IEEE International Conference on Web Services (ICWS), pp. 249–256. IEEE (2015)Google Scholar
  2. 2.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)CrossRefGoogle Scholar
  3. 3.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)Google Scholar
  4. 4.
    Xia, H., Fang, B., Gao, M., Ma, H., Tang, Y., Wen, J.: A novel item anomaly detection approach against shilling attacks in collaborative recommendation systems using the dynamic time interval segmentation technique. Inf. Sci. 306, 150–165 (2015)CrossRefGoogle Scholar
  5. 5.
    Cao, J., Wu, Z., Mao, B., Zhang, Y.: Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web 16(5–6), 729–748 (2013)CrossRefGoogle Scholar
  6. 6.
    Chirita, P.-A., Nejdl, W., Zamfir, C.: Preventing shilling attacks in online recommender systems. In: Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management, pp. 67–74. ACM (2005)Google Scholar
  7. 7.
    Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th International Conference on World Wide Web, pp. 393–402. ACM (2004)Google Scholar
  8. 8.
    Zhang, S., Ouyang, Y., Ford, J., Makedon, F.: Analysis of a low-dimensional linear model under recommendation attacks. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 517–524. ACM (2006)Google Scholar
  9. 9.
    O’Mahony, M.P., Hurley, N.J., Silvestre, G.: Detecting noise in recommender system databases. In: Proceedings of the 11th International Conference on Intelligent User Interfaces, pp. 109–115. ACM (2006)Google Scholar
  10. 10.
    Fu, L., Goh, D.H.-L., Foo, S.S.-B., Na, J.-C.: Collaborative querying through a hybrid query clustering approach. In: Sembok, T.M.T., Zaman, H.B., Chen, H., Urs, S.R., Myaeng, S.-H. (eds.) ICADL 2003. LNCS, vol. 2911, pp. 111–122. Springer, Heidelberg (2003). doi: 10.1007/978-3-540-24594-0_10 CrossRefGoogle Scholar
  11. 11.
    O’Mahony, M.P., Hurley, N.J., Silvestre, G.C.M.: Promoting recommendations: An attack on collaborative filtering. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds.) DEXA 2002. LNCS, vol. 2453, pp. 494–503. Springer, Heidelberg (2002). doi: 10.1007/3-540-46146-9_49 CrossRefGoogle Scholar
  12. 12.
    Grčar, M., Fortuna, B., Mladenič, D., Grobelnik, M.: KNN versus SVM in the collaborative filtering framework. In: Batagelj, V., Bock, H.H., Ferligoj, A., Žiberna, A. (eds.) Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 251–260. Springer, Heidelberg (2006)Google Scholar
  13. 13.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)Google Scholar
  14. 14.
    Lee, C.-H., Kim, Y.-H., Rhee, P.-K.: Web personalization expert with combining collaborative filtering and association rule mining technique. Expert Syst. Appl. 21(3), 131–137 (2001)CrossRefGoogle Scholar
  15. 15.
    Hurley, N., Cheng, Z., Zhang, M.: Statistical attack detection. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 149–156. ACM (2009)Google Scholar
  16. 16.
    Wang, S., Ma, Y., Cheng, B., Chang, R., et al.: Multi-dimensional QoS prediction for service recommendations (2017)Google Scholar
  17. 17.
    Zhang, S., Chakrabarti, A., Ford, J., Makedon, J.: Attack detection in time series for recommender systems. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 809–814. ACM (2006)Google Scholar
  18. 18.
    Zhou, W., Wen, J., Koh, Y.S., Alam, S., Dobbie, G.: Attack detection in recommender systems based on target item analysis. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 332–339. IEEE (2014)Google Scholar
  19. 19.
    Zhou, W., Koh, Y.S., Wen, J., Alam, S., Dobbie, G.: Detection of abnormal profiles on group attacks in recommender systems. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 955–958. ACM (2014)Google Scholar
  20. 20.
    Wu, Z., Wu, J., Cao, J., Tao, D.: HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 985–993. ACM (2012)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Wei Zhou
    • 1
    Email author
  • Junhao Wen
    • 1
  • Qingyu Xiong
    • 1
  • Jun Zeng
    • 1
  • Ling Liu
    • 1
  • Haini Cai
    • 1
  • Tian Chen
    • 1
  1. 1.College of Software EngineeringChongqing UniversityChongqingChina

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