Abstract
Nowadays, collaborative filtering methods have been widely applied to E-commerce platforms. However, due to its openness, a large number of spammers attack those systems to manipulate the recommendation results to earn huge profits. The shilling attack has become a major threat to collaborative filtering systems. Therefore, effectively detecting shilling attacks is a crucial task. Most existing detection methods based on statistical-based features or unsupervised methods rely on a priori knowledge about attack size. Besides, the majority of work focuses on rating attack and ignore the relation attack. In this paper, motivated by the success of heterogeneous information network and oriented towards the hybrid attack, we propose an approach DMD to detect shilling attack based on meta-path and matrix factorization. At first, we concatenate the user-item bipartite network and user-user relation network as a whole. Next, we design several meta-paths to guide the random walk to product node sequences and utilize the skip-gram model to generate user embeddings. Meanwhile, users’ latent factors are decomposed by matrix factorization. Finally, we incorporate these embeddings and factors to joint train the detector. Extensive experimental analysis on two public datasets demonstrate the superiority of the proposed method and show the effectiveness of different attack strategies and various attack sizes.
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References
Bhaumik, R., Williams, C., Mobasher, B., Burke, R.: Securing collaborative filtering against malicious attacks through anomaly detection. In: Proceedings of the 4th Workshop on Intelligent Techniques for Web Personalization (ITWP 2006), Boston, vol. 6, p. 10 (2006)
Hurley, N., Cheng, Z., Zhang, M.: Statistical attack detection. In: Proceedings of the third ACM Conference on Recommender Systems, pp. 149–156. ACM (2009)
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)
Williams, C., Mobasher, B.: Profile injection attack detection for securing collaborative recommender systems. DePaul University CTI Technical Report, pp. 1–47 (2006)
Williams, C.A., Mobasher, B., Burke, R.: Defending recommender systems: detection of profile injection attacks. Serv. Oriented Comput. Appl. 1(3), 157–170 (2007)
Li, W., Gao, M., Li, H., Xiong, Q., Wen, J., Ling, B.: A shilling attack detection algorithm based on popularity degree features. Acta Autom. Sinica 41(9), 1563–1576 (2015)
Zhou, W., Wen, J., Xiong, Q., Gao, M., Zeng, J.: SVM-TIA a shilling attack detection method based on svm and target item analysis in recommender systems. Neurocomputing 210, 197–205 (2016)
Dou, T., Yu, J., Xiong, Q., Gao, M., Song, Y., Fang, Q.: Collaborative shilling detection bridging factorization and user embedding. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds.) CollaborateCom 2017. LNICST, vol. 252, pp. 459–469. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00916-8_43
Mehta, B., Nejdl, W.: Unsupervised strategies for shilling detection and robust collaborative filtering. User Model. User-Adap. Interact. 19(1–2), 65–97 (2009)
Zhang, Y., Tan, Y., Zhang, M., Liu, Y., Chua, T.-S., Ma, S.: Catch the black sheep: Unified framework for shilling attack detection based on fraudulent action propagation. In: IJCAI, pp. 2408–2414 (2015)
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)
Wu, Z., Wang, Y., Wang, Y., Wu, J., Cao, J., Zhang, L.: Spammers detection from product reviews: a hybrid model. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 1039–1044. IEEE (2015)
Junliang, Y., Gao, M., Rong, W., Li, W., Xiong, Q., Wen, J.: Hybrid attacks on model-based social recommender systems. Phys. A: Stati. Mech. Appl. 483, 171–181 (2017)
Yuan, Q., Chen, L., Zhao, S.: Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation. In: Proceedings of the fifth ACM Conference on Recommender Systems, pp. 245–252. ACM (2011)
Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144. ACM (2017)
Song, Y., Gao, M., Yu, J., Xiong, Q.: Social recommendation based on implicit friends discovering via meta-path. In: Proceedings of the 30th International Conference on Tools with Artifical Intelligence (2018)
Sun, Y., Han, J.: Mining heterogeneous information networks: principles and methodologies. Synth. Lect. Data Min. Knowl. Discov. 3(2), 1–159 (2012)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4(11), 992–1003 (2011)
Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Nat. Acad. Sci. 102(46), 16569–16572 (2005)
LĂĽ, L., Zhou, T., Zhang, Q.-M., Stanley, H.E.: The h-index of a network node and its relation to degree and coreness. Nat. commun. 7, 10168 (2016)
Dorogovtsev, S.N., Goltsev, A.V., Mendes, J.F.F.: K-core organization of complex networks. Phys. Rev. Lett. 96(4), 040601 (2006)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)
Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R news 2(3), 18–22 (2002)
Xu, C., Zhang, J., Chang, K., Long, C.: Uncovering collusive spammers in Chinese review websites. In: Proceedings of the 22nd ACM International Conference on Conference on Information and Knowledge Management, pp. 979–988. ACM (2013)
Guo, G., Zhang, J., Yorke-Smith, N.: A novel Bayesian similarity measure for recommender systems. In: IJCAI, pp. 2619–2625 (2013)
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)
Acknowledgments
The work is supported by the Fundamental Research Funds for the Central Universities (106112017CDJXSYY0002).
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Zhang, X., Xiang, H., Song, Y. (2019). Meta-Path and Matrix Factorization Based Shilling Detection for Collaborate Filtering. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_1
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