Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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
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
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)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)
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)
Hurley, N., Cheng, Z., Zhang, M.: Statistical attack detection. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 149–156. ACM (2009)
Wang, S., Ma, Y., Cheng, B., Chang, R., et al.: Multi-dimensional QoS prediction for service recommendations (2017)
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)
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)
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)
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)
Acknowledgement
This research is supported by NSFC under grant No. 61602070, 61502062, 61379158 and China Postdoctoral Science Foundation under Grant No. 2014M560704.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhou, W. et al. (2017). Abnormal Group User Detection in Recommender Systems Using Multi-dimension Time Series. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-59288-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59287-9
Online ISBN: 978-3-319-59288-6
eBook Packages: Computer ScienceComputer Science (R0)