Yashar Deldjoo
Tommaso Di Noia
Felice Antonio Merra
Skip Abstract Section
Abstract
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. However, success has been accompanied with a major new arising challenge: Many applications of machine learning (ML) are adversarial in nature [146]. In recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs.
The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models) and (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 76 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community working on the security of RS or on generative models using GANs to improve their quality.
- Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2020. Multistakeholder recommendation: Survey and research directions. User Model. User-adapt. Interact. 30, 1 (2020), 127--158.Google Scholar
Cross Ref
- Charu C. Aggarwal. 2016. Ensemble-based and hybrid recommender systems. In Recommender Systems. Springer, 199--224.Google Scholar
- Naveed Akhtar and Ajmal Mian. 2018. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access 6 (2018), 14410--14430.Google ScholarCross Ref
- Naveed Akhtar and Ajmal S. Mian. 2018. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access 6 (2018), 14410--14430.Google ScholarCross Ref
- Vito Walter Anelli, Alejandro Bellogín, Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2020. Multi-step adversarial perturbations on recommender systems embeddings. arXiv 2010.01329.Google Scholar
- Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, and Antonio Ferrara. 2019. Towards effective device-aware federated learning. In Proceedings of the International Conference of the Italian Association for Artificial Intelligence. Springer, 477--491.Google ScholarCross Ref
- Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, and Fedelucio Narducci. 2021. How to put users in control of their data in federated top-N recommendation with learning to rank. In Proceedings of the 36th ACM/SIGAPP Symposium on Applied Computing (SAC’21).Google ScholarDigital Library
- Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2020. Adversarial learning for recommendation: Applications for security and generative tasks—concept to code. In Proceedings of the 14th ACM Conference on Recommender Systems. ACM, 738--741.Google ScholarDigital Library
- Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, and Felice Antonio Merra. 2020. Assessing perceptual and recommendation mutation of adversarially poisoned visual recommenders. In Proceedings of the Doctoral Consortium co-located with the Conference of the Italian Association for Artificial Intelligence (DDC@AI*IA’20). CEUR-WS.org.Google Scholar
- Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, and Felice Antonio Merra. 2020. An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders. arxiv:2010.00984Google Scholar
- Martín Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein GAN. CoRR abs/1701.07875 (2017).Google Scholar
- Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo, Hamidreza Alvari, Alexander Nou, and Huan Liu. 2020. Privacy-aware recommendation with private-attribute protection using adversarial learning. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining. 34--42.Google ScholarDigital Library
- Alejandro Bellogin, Pablo Castells, and Ivan Cantador. 2011. Precision-oriented evaluation of recommender systems: An algorithmic comparison. In Proceedings of the 5th ACM International Conference on Recommender Systems. 333--336.Google ScholarDigital Library
- Shlomo Berkovsky and Jill Freyne. 2015. Web personalization and recommender systems. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2307--2308.Google ScholarDigital Library
- David Berthelot, Tom Schumm, and Luke Metz. 2017. BEGAN: Boundary equilibrium generative adversarial networks. CoRR abs/1703.10717 (2017).Google Scholar
- Homanga Bharadhwaj, Homin Park, and Brian Y. Lim. 2018. RecGAN: Recurrent generative adversarial networks for recommendation systems. In Proceedings of the ACM International Conference on Recommender Systems. ACM, 372--376.Google Scholar
- Battista Biggio, Blaine Nelson, and Pavel Laskov. 2012. Poisoning attacks against support vector machines. In Proceedings of the International Conference on Machine Learning (ICML’12).Google Scholar
- Battista Biggio, Konrad Rieck, Davide Ariu, Christian Wressnegger, Igino Corona, Giorgio Giacinto, and Fabio Roli. 2018. Poisoning behavioral malware clustering. arxiv 1811.09985 (2018).Google Scholar
- Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. 2013. Recommender systems survey. Knowl.-based Syst. 46 (2013), 109--132.Google Scholar
- Xiaoyan Cai, Junwei Han, and Libin Yang. 2018. Generative adversarial network based heterogeneous bibliographic network representation for personalized citation recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’18). AAAI Press, 5747--5754.Google Scholar
- Iván Cantador, Ignacio Fernández-Tobías, Shlomo Berkovsky, and Paolo Cremonesi. 2015. Cross-domain recommender systems. In Recommender Systems Handbook. Springer, 919--959.Google ScholarDigital Library
- Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, and Wei Emma Zhang. 2020. Adversarial attacks and detection on reinforcement learning-based interactive recommender systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1669--1672.Google ScholarDigital Library
- Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, and Alexey Kurakin. 2019. On evaluating adversarial robustness. CoRR abs/1902.06705 (2019).Google Scholar
- Nicholas Carlini and David A. Wagner. 2016. Defensive distillation is not robust to adversarial examples. CoRR abs/1607.04311 (2016).Google Scholar
- Nicholas Carlini and David A. Wagner. 2017. Towards evaluating the robustness of neural networks. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE Computer Society, 39--57.Google Scholar
- Pablo Castells, Neil J. Hurley, and Saul Vargas. 2015. Novelty and diversity in recommender systems. In Recommender Systems Handbook. Springer, 881--918.Google Scholar
- Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jaeho Choi. 2019. Rating augmentation with generative adversarial networks towards accurate collaborative filtering. In Proceedings of the World Wide Web Conference. ACM, 2616--2622.Google ScholarDigital Library
- Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018. CFGAN: A generic collaborative filtering framework based on generative adversarial networks. In Proceedings of the Conference on Information and Knowledge Management (CIKM’18). ACM, 137--146.Google ScholarDigital Library
- D. Chae and S. Kim. 2018. Adversarial training of deep autoencoders towards recommendation tasks. In Proceedings of the International Conference on Network Infrastructure and Digital Content (IC-NIDC’18). 91--95.Google Scholar
- Dong-Kyu Chae, Jung Ah Shin, and Sang-Wook Kim. 2019. Collaborative adversarial autoencoders: An effective collaborative filtering model under the GAN framework. IEEE Access 7 (2019), 37650--37663.Google ScholarCross Ref
- Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay. 2018. Adversarial attacks and defences: A survey. CoRR abs/1810.00069 (2018).Google Scholar
- Huiyuan Chen and Jing Li. 2019. Adversarial tensor factorization for context-aware recommendation. In Proceedings of the ACM International Conference on Recommender Systems. ACM, 363--367.Google ScholarDigital Library
- Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020. Bias and debias in recommender system: A survey and future directions. Arxiv Preprint Arxiv:2010.03240 (2020).Google Scholar
- Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng. 2020. A survey of adversarial learning on graphs. CoRR abs/2003.05730 (2020).Google Scholar
- Wang Chen, Hai-Tao Zheng, Yang Wang, Wei Wang, and Rui Zhang. 2019. Utilizing generative adversarial networks for recommendation based on ratings and reviews. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’19). IEEE, 1--8.Google ScholarCross Ref
- Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, and Le Song. 2019. Generative adversarial user model for reinforcement learning based recommendation system. In Proceedings of the International Conference on Machine Learning (ICML’19) (Proc. of Machine Learning Research), Vol. 97. PMLR, 1052--1061.Google Scholar
- Xu Chen, Yongfeng Zhang, Hongteng Xu, Zheng Qin, and Hongyuan Zha. 2019. Adversarial distillation for efficient recommendation with external knowledge. ACM Trans. Inf. Syst. 37, 1 (2019), 12:1--12:28.Google ScholarDigital Library
- Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1724--1734.Google ScholarCross Ref
- Parichat Chonwiharnphan, Pipop Thienprapasith, and Ekapol Chuangsuwanich. 2020. Generating realistic users using generative adversarial network with recommendation-based embedding. IEEE Access 8 (2020), 41384--41393.Google ScholarCross Ref
- Konstantina Christakopoulou and Arindam Banerjee. 2019. Adversarial attacks on an oblivious recommender. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys’19). 322--330.Google ScholarDigital Library
- Rami Cohen, Oren Sar Shalom, Dietmar Jannach, and Amihood Amir. 2020. A Black-box Attack Model for Visually Aware Recommender Systems. arxiv:cs.LG/2011.02701Google Scholar
- Felipe Soares Da Costa and Peter Dolog. 2019. Convolutional adversarial latent factor model for recommender system. In Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference. 419--424.Google Scholar
- Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, and Tommaso Di Noia. 2021. A flexible framework for evaluating user and item fairness in recommender systems. User Model. User-adapt. Interact. (2021), 1--55.Google Scholar
- Yashar Deldjoo, Mihai Gabriel Constantin, Hamid Eghbal-Zadeh, Bogdan Ionescu, Markus Schedl, and Paolo Cremonesi. 2018. Audio-visual encoding of multimedia content for enhancing movie recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems. 455--459.Google ScholarDigital Library
- Yashar Deldjoo, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, and Paolo Cremonesi. 2019. Movie genome: Alleviating new item cold start in movie recommendation. User Model. User-adapt. Interact. 29, 2 (2019), 291--343.Google ScholarDigital Library
- Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2020. How dataset characteristics affect the robustness of collaborative recommendation models. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.Google ScholarDigital Library
- Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2019. Assessing the impact of a user-item collaborative attack on class of users. In Proceedings of the 1st Workshop on the Impact of Recommender Systems co-located with the 13th ACM Conference on Recommender Systems (ImpactRS@RecSys’19).Google Scholar
- Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2020. Adversarial machine learning in recommender systems (AML-RecSys). In Proceedings of the 13th ACM International Conference on Web Search and Data Mining. ACM, 869--872.Google ScholarDigital Library
- Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, and Gabriella Pasi. 2020. Recommender systems leveraging multimedia content. ACM Comput. Surv. 53, 5 (2020), 106:1--106:38.Google ScholarDigital Library
- Yashar Deldjoo, Markus Schedl, Balasz Hidasi, Yinwei Wei, and Xiangnan He. 2020. Multimedia recommender systems. In Recommender Systems Handbook. Springer US.Google Scholar
- Tommaso Di Noia, Daniele Malitesta, and Felice Antonio Merra. 2020. TAaMR: Targeted adversarial attack against multimedia recommender systems. In Proceedings of the Dependable Systems and Networks Workshops. IEEE, 1--8.Google Scholar
- Yali Du, Meng Fang, Jinfeng Yi, Chang Xu, Jun Cheng, and Dacheng Tao. 2019. Enhancing the robustness of neural collaborative filtering systems under malicious attacks. IEEE Trans. Multim. 21, 3 (2019), 555--565.Google ScholarCross Ref
- Cynthia Dwork. 2006. Differential privacy. In Proceedings of the International Colloquium on Automata, Languages and Programming (ICALP’06) (Lecture Notes in Computer Science), Vol. 4052. Springer, 1--12.Google ScholarDigital Library
- Michael D. Ekstrand, John Riedl, and Joseph A. Konstan. 2011. Collaborative filtering recommender systems. Found. Trends Hum.-comput. Interact. 4, 2 (2011), 175--243.Google Scholar
- Wenqi Fan, Tyler Derr, Yao Ma, Jianping Wang, Jiliang Tang, and Qing Li. 2019. Deep adversarial social recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 1351--1357.Google ScholarCross Ref
- Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor S. Lempitsky. 2017. Domain-adversarial training of neural networks. In Domain Adaptation in Computer Vision Applications. Springer, 189--209.Google Scholar
- Guangyu Gao, Liling Liu, Li Wang, and Yihang Zhang. 2019. Fashion clothes matching scheme based on siamese network and autoencoder. Multim. Syst. 25, 6 (2019), 593--602.Google ScholarCross Ref
- Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2016. Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 2414--2423.Google ScholarCross Ref
- David Goldberg, David A. Nichols, Brian M. Oki, and Douglas B. Terry. 1992. Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 12 (1992), 61--70.Google ScholarDigital Library
- Felipe González, Yihan Yu, Andrea Figueroa, Claudia López, and Cecilia R. Aragon. 2019. Global reactions to the Cambridge Analytica scandal: A cross-language social media study. In Proceedings of the World Wide Web Conference. ACM, 799--806.Google Scholar
- Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative adversarial networks. CoRR abs/1406.2661 (2014).Google Scholar
- Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In Proceedings of the 3rd International Conference on Learning Representations (ICLR’15)Google Scholar
- Ihsan Gunes, Cihan Kaleli, Alper Bilge, and Huseyin Polat. 2014. Shilling attacks against recommender systems: A comprehensive survey. Artif. Intell. Rev. 42, 4 (2014), 767--799.Google ScholarDigital Library
- Junwei Han, Dingwen Zhang, Gong Cheng, Nian Liu, and Dong Xu. 2018. Advanced deep-learning techniques for salient and category-specific object detection: A survey. IEEE Sig. Proc. Mag. 35, 1 (2018), 84--100.Google ScholarCross Ref
- Gaole He, Junyi Li, Wayne Xin Zhao, Peiju Liu, and Ji-Rong Wen. 2020. Mining implicit entity preference from user-item interaction data for knowledge graph completion via adversarial learning. In Proceedings of the WWW. ACM, 740--751.Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 770--778.Google ScholarCross Ref
- Ruining He and Julian J. McAuley. 2016. VBPR: Visual Bayesian personalized ranking from implicit feedback. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 144--150.Google Scholar
- Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial personalized ranking for recommendation. In Proceedings of the SIGIR Conference on Research and Development in Information Retrieval. ACM, 355--364.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the World Wide Web Conference. ACM, 173--182.Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR’16).Google Scholar
- Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. 2015. Distilling the knowledge in a neural network. CoRR abs/1503.02531 (2015).Google Scholar
- Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, and J. D. Tygar. 2011. Adversarial machine learning. In Proceedings of the ACM Workshop on Artificial Intelligence and Security. ACM, 43--58.Google Scholar
- Cong Phuoc Huynh, Arridhana Ciptadi, Ambrish Tyagi, and Amit Agrawal. 2018. CRAFT: Complementary recommendation by adversarial feature transform. In Proceedings of the European Conference on Computer Vision Workshops (Lecture Notes in Computer Science), Vol. 11131. Springer, 54--66.Google Scholar
- Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical reparameterization with Gumbel-Softmax. In Proceedings of the International Conference on Learning Representations (Poster).Google Scholar
- Sang-Young Jo, Sun-Hye Jang, Hee-Eun Cho, and Jin-Woo Jeong. 2019. Scenery-based fashion recommendation with cross-domain geneartive adverserial networks. In Proceedings of the International Conference on Big Data and Smart Computing. IEEE, 1--4.Google ScholarCross Ref
- Zach Jorgensen, Yan Zhou, and W. Meador Inge. 2008. A multiple instance learning strategy for combating good word attacks on spam filters. J. Mach. Learn. Res. 9 (2008), 1115--1146.Google ScholarDigital Library
- Rafal Józefowicz, Wojciech Zaremba, and Ilya Sutskever. 2015. An empirical exploration of recurrent network architectures. In Proceedings of the International Conference on Machine Learning (ICML’15).Google Scholar
- Marius Kaminskas and Derek Bridge. 2016. Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7, 1 (2016), 1--42.Google ScholarDigital Library
- Wang-Cheng Kang, Chen Fang, Zhaowen Wang, and Julian J. McAuley. 2017. Visually aware fashion recommendation and design with generative image models. In Proceedings of the IEEE International Conference on Data Mining (ICDM’17). 207--216.Google Scholar
- Michal Kompan, Ondrej Kassák, and Mária Bieliková. 2017. Beyond user preferences: The short-term behaviour modelling. In Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems co-located with the 11th International Conference on Recommender Systems (RecSys’17). 1--3.Google Scholar
- Yehuda Koren. 2010. Collaborative filtering with temporal dynamics. Commun. ACM 53, 4 (2010), 89--97.Google ScholarDigital Library
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. IEEE Comput. 42, 8 (2009), 30--37.Google ScholarDigital Library
- Adit Krishnan, Hari Cheruvu, Tao Cheng, and Hari Sundaram. 2019. A modular adversarial approach to social recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM’19).Google ScholarDigital Library
- Adit Krishnan, Ashish Sharma, Aravind Sankar, and Hari Sundaram. 2018. An adversarial approach to improve long-tail performance in neural collaborative filtering. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM’18).Google ScholarDigital Library
- Sudhir Kumar and Mithun Das Gupta. 2019. c+GAN: Complementary fashion item recommendation. In Proceedings of the Workshop on AI for Fashion.Google Scholar
- Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2017. Adversarial examples in the physical world. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17).Google Scholar
- Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2017. Adversarial machine learning at scale. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17).Google Scholar
- Wonsung Lee, Kyungwoo Song, and Il-Chul Moon. 2017. Augmented variational autoencoders for collaborative filtering with auxiliary information. In Proceedings of the Conference on Information and Knowledge Management (CIKM’17). ACM, 1139--1148.Google ScholarDigital Library
- Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik. 2016. Data poisoning attacks on factorization-based collaborative filtering. In Proceedings of the Conference on Neural Information Processing Systems. 1885--1893.Google Scholar
- Ruirui Li, Liangda Li, Xian Wu, Yunhong Zhou, and Wei Wang. 2019. Click feedback-aware query recommendation using adversarial examples. In Proceedings of the World Wide Web Conference. ACM, 2978--2984.Google ScholarDigital Library
- Ruirui Li, Xian Wu, and Wei Wang. 2020. Adversarial learning to compare: Self-attentive prospective customer recommendation in location based social networks. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining. 349--357.Google ScholarDigital Library
- Ying Li, Jia-Jie Xu, Pengpeng Zhao, Junhua Fang, Wei Chen, and Lei Zhao. 2020. ATLRec: An attentional adversarial transfer learning network for cross-domain recommendation. J. Comput. Sci. Technol. 35, 4 (2020), 794--808.Google ScholarDigital Library
- Zhaoqiang Li, Jiajin Huang, and Ning Zhong. 2018. Leveraging reconstructive profiles of users and items for tag-aware recommendation. In Proceedings of the IEEE International Conference on Data Mining Workshops. IEEE, 1294--1299.Google ScholarCross Ref
- Jianxun Lian, Fuzheng Zhang, Xing Xie, and Guangzhong Sun. 2017. CCCFNet: A content-boosted collaborative filtering neural network for cross domain recommender systems. In Proceedings of the World Wide Web Conference. ACM, 817--818.Google ScholarDigital Library
- Chen Lin, Si Chen, Hui Li, Yanghua Xiao, Lianyun Li, and Qian Yang. 2020. Attacking recommender systems with augmented user profiles. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 855--864.Google ScholarDigital Library
- Jixiong Liu, Weike Pan, and Zhong Ming. 2020. CoFiGAN: Collaborative filtering by generative and discriminative training for one-class recommendation. Knowl.-based Syst. 191 (2020), 105255.Google Scholar
- Ming-Yu Liu, Thomas Breuel, and Jan Kautz. 2017. Unsupervised image-to-image translation networks. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 700--708.Google Scholar
- Qiang Liu, Pan Li, Wentao Zhao, Wei Cai, Shui Yu, and Victor C. M. Leung. 2018. A survey on security threats and defensive techniques of machine learning: A data driven view. IEEE Access 6 (2018), 12103--12117.Google ScholarCross Ref
- Wei Liu, Zhi-Jie Wang, Bin Yao, and Jian Yin. 2019. Geo-ALM: POI recommendation by fusing geographical information and adversarial learning mechanism. In Proceedings of the International Joint Conference on Artificial Intelligence. 1807--1813.Google ScholarCross Ref
- Yang Liu, Xianzhuo Xia, Liang Chen, Xiangnan He, Carl Yang, and Zibin Zheng. 2020. Certifiable robustness to discrete adversarial perturbations for factorization machines. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’20).Google ScholarDigital Library
- Zhuoran Liu and Martha A. Larson. 2020. Adversarial item promotion: Vulnerabilities at the core of top-n recommenders that use images to address cold start. arXiv 2006.01888.Google Scholar
- Gilles Louppe, Michael Kagan, and Kyle Cranmer. 2017. Learning to pivot with adversarial networks. In Proceedings of the Conference on Advances in Neural Information Processing Systems, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 981--990.Google Scholar
- Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards deep learning models resistant to adversarial attacks. In Proceedings of the 6th International Conference on Learning Representations (ICLR’18).Google Scholar
- Jarana Manotumruksa and Emine Yilmaz. 2020. Sequential-based adversarial optimisation for personalised top-n item recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’20).Google ScholarDigital Library
- Xudong Mao, Qing Li, Haoran Xie, Raymond Y. K. Lau, Zhen Wang, and Stephen Paul Smolley. 2017. Least squares generative adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 2813--2821.Google ScholarCross Ref
- Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 43--52.Google Scholar
- Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, and Yujun Zhang. 2018. Personalized privacy-preserving social recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’18). AAAI Press, 3796--3803.Google Scholar
- Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, and Yujun Zhang. 2019. Towards privacy preserving social recommendation under personalized privacy settings. World Wide Web 22, 6 (2019), 2853--2881.Google ScholarCross Ref
- Lars M. Mescheder, Sebastian Nowozin, and Andreas Geiger. 2017. Adversarial variational Bayes: Unifying variational autoencoders and generative adversarial networks. In Proceedings of the International Conference on Machine Learning (ICML’17) (Proceedings of Machine Learning Research), Vol. 70. PMLR, 2391--2400.Google Scholar
- Weiqing Min, Bing-Kun Bao, Changsheng Xu, and M. Shamim Hossain. 2015. Cross-platform multi-modal topic modeling for personalized inter-platform recommendation. IEEE Trans. Multim. 17, 10 (2015), 1787--1801.Google ScholarDigital Library
- Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. CoRR abs/1411.1784 (2014).Google Scholar
- Linh Nguyen and Tsukasa Ishigaki. 2018. Domain-to-domain translation model for recommender system. CoRR abs/1812.06229 (2018).Google Scholar
- Augustus Odena, Christopher Olah, and Jonathon Shlens. 2017. Conditional image synthesis with auxiliary classifier GANs. In Proceedings of the International Conference on Machine Learning (ICML’17) (Proceedings of Machine Learning Research), Vol. 70. PMLR, 2642--2651.Google Scholar
- Ivan Palomares, Carlos Porcel, Luiz Pizzato, Ido Guy, and Enrique Herrera-Viedma. 2020. Reciprocal recommender systems: Analysis of state-of-art literature, challenges and opportunities on social recommendation. Arxiv Preprint Arxiv:2007.16120 (2020).Google Scholar
- Zhaoqing Pan, Weijie Yu, Xiaokai Yi, Asifullah Khan, Feng Yuan, and Yuhui Zheng. 2019. Recent progress on generative adversarial networks (GANs): A survey. IEEE Access 7 (2019), 36322--36333.Google ScholarCross Ref
- Nicolas Papernot, Patrick D. McDaniel, and Ian J. Goodfellow. 2016. Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. CoRR abs/1605.07277 (2016).Google Scholar
- Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. 2016. Distillation as a defense to adversarial perturbations against deep neural networks. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE Computer Society, 582--597.Google ScholarCross Ref
- Dae Hoon Park and Yi Chang. 2019. Adversarial sampling and training for semi-supervised information retrieval. In Proceedings of the World Wide Web Conference (WWW’19). 1443--1453.Google ScholarDigital Library
- Dilruk Perera and Roger Zimmermann. 2019. CnGAN: Generative adversarial networks for cross-network user preference generation for non-overlapped users. In Proceedings of the World Wide Web Conference. ACM, 3144--3150.Google ScholarDigital Library
- Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence-aware recommender systems. ACM Comput. Surv. 51, 4 (2018), 66:1--66:36.Google ScholarDigital Library
- Dimitrios Rafailidis and Fabio Crestani. 2019. Adversarial training for review-based recommendations. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). 1057--1060.Google ScholarDigital Library
- Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin Ding, and Ji-Rong Wen. 2020. Sequential recommendation with self-attentive multi-adversarial network. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’20).Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI’09). 452--461.Google ScholarDigital Library
- Steffen Rendle and Lars Schmidt-Thieme. 2010. Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the International Conference on Web Search and Data Mining. ACM, 81--90.Google ScholarDigital Library
- Yehezkel S. Resheff, Yanai Elazar, Moni Shahar, and Oren Sar Shalom. 2019. Privacy and fairness in recommender systems via adversarial training of user representations. In Proceedings of the International Conference on Pattern Recognition Applications and Methods. SciTePress, 476--482.Google ScholarCross Ref
- Andras Rozsa, Ethan M. Rudd, and Terrance E. Boult. 2016. Adversarial diversity and hard positive generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR’16). IEEE Computer Society, 410--417.Google Scholar
- Ruslan Salakhutdinov and Geoffrey E. Hinton. 2009. Semantic hashing. Int. J. Approx. Reas. 50, 7 (2009), 969--978.Google ScholarDigital Library
- Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training GANs. In Proceedings of the Conference on Neural Information Processing Systems. 2226--2234.Google Scholar
- Dandan Sha, Daling Wang, Xiangmin Zhou, Shi Feng, Yifei Zhang, and Ge Yu. 2016. An approach for clothing recommendation based on multiple image attributes. In Proceedings of the 17th International Conference on Web-Age Information Management (WAIM’16) (Lecture Notes in Computer Science), Bin Cui, Nan Zhang, Jianliang Xu, Xiang Lian, and Dexi Liu (Eds.), Vol. 9658. Springer, 272--285.Google ScholarCross Ref
- Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Comput. Surv. 47, 1 (2014), 3:1--3:45.Google ScholarDigital Library
- Yue Shi, Martha A. Larson, and Alan Hanjalic. 2010. List-wise learning to rank with matrix factorization for collaborative filtering. In Proceedings of the ACM International Conference on Recommender Systems. ACM, 269--272.Google ScholarDigital Library
- Yong-Siang Shih, Kai-Yueh Chang, Hsuan-Tien Lin, and Min Sun. 2018. Compatibility family learning for item recommendation and generation. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’18). AAAI Press, 2403--2410.Google Scholar
- Hyejin Shin, Sungwook Kim, Junbum Shin, and Xiaokui Xiao. 2018. Privacy enhanced matrix factorization for recommendation with local differential privacy. IEEE Trans. Knowl. Data Eng. 30, 9 (2018), 1770--1782.Google ScholarDigital Library
- Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar. 2018. A unified approach to quantifying algorithmic unfairness: Measuring individual & group unfairness via inequality indices. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2239--2248.Google ScholarDigital Library
- Changfeng Sun, Han Liu, Meng Liu, Zhaochun Ren, Tian Gan, and Liqiang Nie. 2020. LARA: Attribute-to-feature adversarial learning for new-item recommendation. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining. 582--590.Google ScholarDigital Library
- Zhongchuan Sun, Bin Wu, Yunpeng Wu, and Yangdong Ye. 2019. APL: Adversarial pairwise learning for recommender systems. Expert Syst. Appl. 118 (2019), 573--584.Google ScholarCross Ref
- Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour. 1999. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of the Conference on Neural Information Processing Systems. The MIT Press, 1057--1063.Google Scholar
- Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. 4278--4284.Google Scholar
- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In Proceedings of the International Conference on Learning Representations.Google Scholar
- J. Tang, X. Du, X. He, F. Yuan, Q. Tian, and T. Chua. 2019. Adversarial training towards robust multimedia recommender system. IEEE Trans. Knowl. Data Eng. (2019), 1--1.Google Scholar
- Yuzhen Tong, Yadan Luo, Zheng Zhang, Shazia Sadiq, and Peng Cui. 2019. Collaborative generative adversarial network for recommendation systems. In Proceedings of the IEEE International Conference on Data Engineering Workshops. IEEE, 161--168.Google ScholarCross Ref
- Thanh Tran, Renee Sweeney, and Kyumin Lee. 2019. Adversarial Mahalanobis distance-based attentive song recommender for automatic playlist continuation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). 245--254.Google ScholarDigital Library
- Iacopo Vagliano, Lukas Galke, Florian Mai, and Ansgar Scherp. 2018. Using adversarial autoencoders for multi-modal automatic playlist continuation. In Proceedings of the ACM International Conference on Recommender Systems Challenge. ACM, 5:1--5:6.Google ScholarDigital Library
- Sahil Verma and Julia Rubin. 2018. Fairness definitions explained. In Proceedings of the International Workshop on Software Fairness (FairWare@ICSE’18). ACM, 1--7.Google ScholarDigital Library
- Riccardo Volpi, Pietro Morerio, Silvio Savarese, and Vittorio Murino. 2018. Adversarial feature augmentation for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). IEEE Computer Society, 5495--5504. DOI:https://doi.org/10.1109/CVPR.2018.00576Google ScholarCross Ref
- Yevgeniy Vorobeychik and Murat Kantarcioglu. 2018. Adversarial Machine Learning. Morgan & Claypool Publishers.Google Scholar
- C. Wang, M. Niepert, and H. Li. 2020. RecSys-DAN: Discriminative adversarial networks for cross-domain recommender systems. IEEE Trans. Neural Netw. Learn. Syst. 31, 8 (2020), 2731--2740.Google ScholarCross Ref
- Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. GraphGAN: Graph representation learning with generative adversarial nets. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’18). AAAI Press, 2508--2515.Google Scholar
- Jianfang Wang and Pengfei Han. 2020. Adversarial training-based mean Bayesian personalized ranking for recommender system. IEEE Access 8 (2020), 7958--7968.Google ScholarCross Ref
- Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017. IRGAN: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the SIGIR Conference on Research and Development in Information Retrieval. ACM, 515--524.Google ScholarDigital Library
- Ke Wang, Janak J. Parekh, and Salvatore J. Stolfo. 2006. Anagram: A content anomaly detector resistant to mimicry attack. In Proceedings of the 9th International Symposium on Recent Advances in Intrusion Detection (RAID’06) (Lecture Notes in Computer Science), Vol. 4219. Springer, 226--248.Google Scholar
- Qinyong Wang, Hongzhi Yin, Zhiting Hu, Defu Lian, Hao Wang, and Zi Huang. 2018. Neural memory streaming recommender networks with adversarial training. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2467--2475.Google ScholarDigital Library
- Qinyong Wang, Hongzhi Yin, Hao Wang, Quoc Viet Hung Nguyen, Zi Huang, and Lizhen Cui. 2019. Enhancing collaborative filtering with generative augmentation. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 548--556.Google ScholarDigital Library
- Yang Wang, Hai-Tao Zheng, Wang Chen, and Rui Zhang. 2019. LambdaGAN: Generative adversarial nets for recommendation task with lambda strategy. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1--8.Google ScholarCross Ref
- Zongwei Wang, Min Gao, Xinyi Wang, Junliang Yu, Junhao Wen, and Qingyu Xiong. 2019. A minimax game for generative and discriminative sample models for recommendation. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (Lecture Notes in Computer Science), Vol. 11440. Springer, 420--431.Google ScholarDigital Library
- Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, and Archy de Berker. 2019. Adversarial examples in modern machine learning: A review. CoRR abs/1911.05268 (2019).Google Scholar
- Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing. 2017. Recurrent recommender networks. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM’17). 495--503.Google ScholarDigital Library
- Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, and Xing Xie. 2019. Neural news recommendation with multi-head self-attention. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19).Google ScholarCross Ref
- Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2020. Fairness-aware news recommendation with decomposed adversarial learning. arXiv 2006.16742.Google Scholar
- Qiong Wu, Yong Liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, and Lu Guan. 2019. PD-GAN: Adversarial learning for personalized diversity-promoting recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 3870--3876.Google ScholarCross Ref
- Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 153--162.Google ScholarDigital Library
- Huang Xiao, Battista Biggio, Blaine Nelson, Han Xiao, Claudia Eckert, and Fabio Roli. 2015. Support vector machines under adversarial label contamination. Neurocomputing 160 (2015), 53--62. DOI:https://doi.org/10.1016/j.neucom.2014.08.081Google ScholarCross Ref
- Han Xiao, Huang Xiao, and Claudia Eckert. 2012. Adversarial label flips attack on support vector machines. In Proceedings of the European Conference on Artificial Intelligence (Frontiers in Artificial Intelligence and Applications).Google Scholar
- Dingqi Yang, Bingqing Qu, and Philippe Cudré-Mauroux. 2019. Privacy-preserving social media data publishing for personalized ranking-based recommendation. IEEE Trans. Knowl. Data Eng. 31, 3 (2019), 507--520.Google ScholarDigital Library
- Zilin Yang, Zhuo Su, Yang Yang, and Ge Lin. 2018. From recommendation to generation: A novel fashion clothing advising framework. Proceedings of the 7th International Conference on Digital Home (ICDH’18) 1, 1 (2018), 180--186.Google ScholarCross Ref
- Jin Yi, Jiajin Huang, and Jin Qin. 2018. Rating prediction in review-based recommendations via adversarial auto-encoder. In Proceedings of the International Conference on Web Intelligence. IEEE Computer Society, 144--149.Google ScholarCross Ref
- Ruiping Yin, Kan Li, Jie Lu, and Guangquan Zhang. 2019. RsyGAN: Generative adversarial network for recommender systems. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1--7.Google ScholarCross Ref
- Junliang Yu, Min Gao, Jundong Li, Chongming Gao, and Qinyong Wang. 2019. Generating reliable friends via adversarial training to improve social recommendation. CoRR abs/1909.03529 (2019).Google Scholar
- Xianwen Yu, Xiaoning Zhang, Yang Cao, and Min Xia. 2019. VAEGAN: A collaborative filtering framework based on adversarial variational autoencoders. In Proceedings of the International Joint Conference on Artificial Intelligence. ijcai.org, 4206--4212.Google ScholarCross Ref
- Fajie Yuan, Guibing Guo, Joemon M. Jose, Long Chen, Haitao Yu, and Weinan Zhang. 2016. LambdaFM: Learning optimal ranking with factorization machines using lambda surrogates. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM’16). 227--236.Google ScholarDigital Library
- Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. Adversarial collaborative auto-encoder for top-n recommendation. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’19). 1--8.Google ScholarCross Ref
- Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. Adversarial collaborative neural network for robust recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). 1065--1068.Google ScholarDigital Library
- Feng Yuan, Lina Yao, and Boualem Benatallah. 2020. Exploring missing interactions: A convolutional generative adversarial network for collaborative filtering. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 1773--1782.Google ScholarDigital Library
- G. Zhang, Y. Liu, and X. Jin. 2018. Adversarial variational autoencoder for top-n recommender systems. In Proceedings of the IEEE 9th International Conference on Software Engineering and Service Science (ICSESS’18). 853--856.Google Scholar
- Jiani Zhang, Xingjian Shi, Irwin King, and Dit-Yan Yeung. 2017. Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). 765--774.Google ScholarDigital Library
- Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv. 52, 1 (2019), 5:1--5:38.Google ScholarDigital Library
- Ye Zhang, Libin Yang, Xiaoyan Cai, and Hang Dai. 2018. A novel personalized citation recommendation approach based on GAN. In Proceedings of the International Symposium on Methodologies for Intelligent Systems (Lecture Notes in Computer Science), Vol. 11177. Springer, 268--278.Google ScholarDigital Library
- Pengyu Zhao, Tianxiao Shui, Yuanxing Zhang, Kecheng Xiao, and Kaigui Bian. 2020. Adversarial oracular seq2seq learning for sequential recommendation. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI’20).Google ScholarCross Ref
- W. Zhao, B. Wang, M. Yang, J. Ye, Z. Zhao, X. Chen, and Y. Shen. 2020. Leveraging long and short-term information in content-aware movie recommendation via adversarial training. IEEE Trans. Cyber. 50, 11 (2020), 4680--4693.Google ScholarCross Ref
- Wei Zhao, Benyou Wang, Jianbo Ye, Yongqiang Gao, Min Yang, and Xiaojun Chen. 2018. PLASTIC: Prioritize long and short-term information in top-n recommendation using adversarial training. In Proceedings of the International Joint Conference on Artificial Intelligence. ijcai.org, 3676--3682.Google ScholarCross Ref
- Fan Zhou, Ruiyang Yin, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Jin Wu. 2019. Adversarial point-of-interest recommendation. In Proceedings of the World Wide Web Conference. ACM, 3462--34618.Google ScholarDigital Library
- Renjie Zhou, Samamon Khemmarat, and Lixin Gao. 2010. The impact of YouTube recommendation system on video views. In Proceedings of the 10th ACM SIGCOMM Internet Measurement Conference (IMC’10). 404--410.Google ScholarDigital Library
- Shizhan Zhu, Sanja Fidler, Raquel Urtasun, Dahua Lin, and Chen Change Loy. 2017. Be your own Prada: Fashion synthesis with structural coherence. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). IEEE Computer Society, 1689--1697.Google ScholarCross Ref
- Ziwei Zhu, Jianling Wang, and James Caverlee. 2020. Measuring and mitigating item under-recommendation bias in personalized ranking systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’20). ACM, 449--458.Google ScholarDigital Library
Index Terms
- A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks
Recommendations
Poisoning Attacks to Graph-Based Recommender Systems
ACSAC '18: Proceedings of the 34th Annual Computer Security Applications ConferenceRecommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a ...
Adversarial attacks to API recommender systems: time to wake up and smell the coffee?
ASE '21: Proceedings of the 36th IEEE/ACM International Conference on Automated Software EngineeringRecommender systems in software engineering provide developers with a wide range of valuable items to help them complete their tasks. Among others, API recommender systems have gained momentum in recent years as they became more successful at suggesting ...
Adversarial Attacks and Defenses in Deep Learning: A Survey
Intelligent Computing Theories and ApplicationAbstractIn recent years, researches on adversarial attacks and defense mechanisms have obtained much attention. It's observed that adversarial examples crafted with small perturbations would mislead the deep neural network (DNN) model to output wrong ...
Comments