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Adomavicius and A. Tuzhilin, \u201cToward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,\u201d IEEE Trans. Knowl. Data Eng., vol.17, no.6, pp.734-749, 2005. 10.1109\/tkde.2005.99","DOI":"10.1109\/TKDE.2005.99"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] S. Zhang, L. Yao, A. Sun, and Y. Tay, \u201cDeep learning based recommender system: A survey and new perspectives,\u201d ACM Computing Surveys (CSUR), vol.52, no.1, pp.1-38, 2019. 10.1145\/3285029","DOI":"10.1145\/3285029"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] C. Shi, Z. Zhang, P. Luo, P.S. Yu, Y. Yue, and B. Wu, \u201cSemantic path based personalized recommendation on weighted heterogeneous information networks,\u201d Proc. 24th ACM International on Conference on Information and Knowledge Management, pp.453-462, 2015. 10.1145\/2806416.2806528","DOI":"10.1145\/2806416.2806528"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Z. Wang, H. Liu, Y. Du, Z. Wu, and X. Zhang, \u201cUnified embedding model over heterogeneous information network for personalized recommendation,\u201d IJCAI, pp.3813-3819, 2019. 10.24963\/ijcai.2019\/529","DOI":"10.24963\/ijcai.2019\/529"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] S. Fan, J. Zhu, X. Han, C. Shi, L. Hu, B. Ma, and Y. Li, \u201cMetapath-guided heterogeneous graph neural network for intent recommendation,\u201d Proc. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.2478-2486, 2019. 10.1145\/3292500.3330673","DOI":"10.1145\/3292500.3330673"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] M. Si and Q. Li, \u201cShilling attacks against collaborative recommender systems: a review,\u201d Artificial Intelligence Review, vol.53, no.1, pp.291-319, 2020. 10.1007\/s10462-018-9655-x","DOI":"10.1007\/s10462-018-9655-x"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] S.K. Lam and J. Riedl, \u201cShilling recommender systems for fun and profit,\u201d Proc. 13th international conference on World Wide Web, pp.393-402, 2004. 10.1145\/988672.988726","DOI":"10.1145\/988672.988726"},{"key":"8","unstructured":"[8] M.P. O'Mahony, Towards robust and efficient automated collaborative filtering, Ph.D. thesis, Citeseer, 2004."},{"key":"9","unstructured":"[9] B. Li, Y. Wang, A. Singh, and Y. Vorobeychik, \u201cData poisoning attacks on factorization-based collaborative filtering,\u201d arXiv preprint arXiv:1608.08182, 2016."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] M. Fang, G. Yang, N.Z. Gong, and J. Liu, \u201cPoisoning attacks to graph-based recommender systems,\u201d Proc. 34th Annual Computer Security Applications Conference, pp.381-392, 2018. 10.1145\/3274694.3274706","DOI":"10.1145\/3274694.3274706"},{"key":"11","unstructured":"[11] Y. Deldjoo, T. Di Noia, and F.A. Merra, \u201cAssessing the impact of a user-item collaborative attack on class of users,\u201d arXiv preprint arXiv:1908.07968, 2019."},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] K. Chen, P.P. Chan, F. Zhang, and Q. Li, \u201cShilling attack based on item popularity and rated item correlation against collaborative filtering,\u201d International Journal of Machine Learning and Cybernetics, vol.10, no.7, pp.1833-1845, 2019. 10.1007\/s13042-018-0861-2","DOI":"10.1007\/s13042-018-0861-2"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] C. Shi, B. Hu, W.X. Zhao, and S.Y. Philip, \u201cHeterogeneous information network embedding for recommendation,\u201d IEEE Trans. Knowl. Data Eng., vol.31, no.2, pp.357-370, 2018. 10.1109\/tkde.2018.2833443","DOI":"10.1109\/TKDE.2018.2833443"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] Y. Sun, J. Han, X. Yan, P.S. Yu, and T. Wu, \u201cPathsim: Meta path-based top-k similarity search in heterogeneous information networks,\u201d Proc. VLDB Endowment, vol.4, no.11, pp.992-1003, 2011. 10.14778\/3402707.3402736","DOI":"10.14778\/3402707.3402736"},{"key":"15","unstructured":"[15] J.S. Breese, D. Heckerman, and C. Kadie, \u201cEmpirical analysis of predictive algorithms for collaborative filtering,\u201d arXiv preprint arXiv:1301.7363, 2013."},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] M. Deshpande and G. Karypis, \u201cItem-based top-n recommendation algorithms,\u201d ACM Transactions on Information Systems (TOIS), vol.22, no.1, pp.143-177, 2004. 10.1145\/963770.963776","DOI":"10.1145\/963770.963776"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] H. Polat and W. Du, \u201cSvd-based collaborative filtering with privacy,\u201d Proc. 2005 ACM symposium on Applied computing, pp.791-795, 2005. 10.1145\/1066677.1066860","DOI":"10.1145\/1066677.1066860"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] F. Ortega, A. Hernando, J. Bobadilla, and J.H. Kang, \u201cRecommending items to group of users using matrix factorization based collaborative filtering,\u201d Information Sciences, vol.345, pp.313-324, 2016. 10.1016\/j.ins.2016.01.083","DOI":"10.1016\/j.ins.2016.01.083"},{"key":"19","unstructured":"[19] Z.h. Xu and B.y. Wang, \u201cCollaborative filtering algorithm based on item complex similarity,\u201d Application Research of Computers, p.02, 2014."},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] C. Shi, J. Liu, F. Zhuang, S.Y. Philip, and B. Wu, \u201cIntegrating heterogeneous information via flexible regularization framework for recommendation,\u201d Knowledge and Information Systems, vol.49, no.3, pp.835-859, 2016. 10.1007\/s10115-016-0925-0","DOI":"10.1007\/s10115-016-0925-0"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] J. Zheng, J. Liu, C. Shi, F. Zhuang, J. Li, and B. Wu, \u201cRecommendation in heterogeneous information network via dual similarity regularization,\u201d International Journal of Data Science and Analytics, vol.3, no.1, pp.35-48, 2017. 10.1007\/s41060-016-0031-0","DOI":"10.1007\/s41060-016-0031-0"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] J. Zheng, J. Liu, C. Shi, F. Zhuang, J. Li, and B. Wu, \u201cDual similarity regularization for recommendation,\u201d Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.542-554, Springer, 2016. 10.1007\/978-3-319-31750-2_43","DOI":"10.1007\/978-3-319-31750-2_43"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] N.J. Hurley, M.P. O'Mahony, and G.C. Silvestre, \u201cAttacking recommender systems: A cost-benefit analysis,\u201d IEEE Intell. Syst., vol.22, no.3, pp.64-68, 2007. 10.1109\/mis.2007.44","DOI":"10.1109\/MIS.2007.44"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] Z. Wu, J. Wu, J. Cao, and D. Tao, \u201cHysad: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation,\u201d Proc. 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.985-993, 2012. 10.1145\/2339530.2339684","DOI":"10.1145\/2339530.2339684"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] Z. Yang, Z. Cai, and X. Guan, \u201cEstimating user behavior toward detecting anomalous ratings in rating systems,\u201d Knowledge-Based Systems, vol.111, pp.144-158, 2016. 10.1016\/j.knosys.2016.08.011","DOI":"10.1016\/j.knosys.2016.08.011"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] H. Xu, Y. Ma, H.-C. Liu, D. Deb, H. Liu, J.-L. Tang, and A.K. Jain, \u201cAdversarial attacks and defenses in images, graphs and text: A review,\u201d International Journal of Automation and Computing, vol.17, no.2, pp.151-178, 2020. 10.1007\/s11633-019-1211-x","DOI":"10.1007\/s11633-019-1211-x"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] Q. Zhou, Y. Ren, T. Xia, L. Yuan, and L. Chen, \u201cData poisoning attacks on graph convolutional matrix completion,\u201d International Conference on Algorithms and Architectures for Parallel Processing, pp.427-439, Springer, 2019. 10.1007\/978-3-030-38961-1_38","DOI":"10.1007\/978-3-030-38961-1_38"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] B. Mobasher, R. Burke, R. Bhaumik, and C. 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