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Federated Recommendation (FR) has received considerable popularity and attention in the past few years.
T. Bai, J.-R. Wen, J. Zhang, and W. X. Zhao, “A neural collaborative filtering model with interaction-based neighborhood,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , 2017, pp. 1979–1982
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Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer , vol. 42, no. 8, pp. 30–37, 2009
2009
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S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “BPR: bayesian personalized ranking from implicit feedback,” in UAI . AUAI Press, 2009, pp. 452–461
2009
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S. Kabbur, X. Ning, and G. Karypis, “Fism: factored item similarity models for top-n recommender systems,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining , 2013, pp. 659–667
2013
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R. Kumar, B. Verma, and S. S. Rastogi, “Social popularity based svd++ recommender system,” International Journal of Computer Applications , vol. 87, no. 14, 2014
2014
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I. Gunes, C. Kaleli, A. Bilge, and H. Polat, “Shilling attacks against recommender systems: a comprehensive survey,” Artificial Intelligence Review , vol. 42, no. 4, pp. 767–799, 2014
2014
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2015
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2015
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R. He and J. McAuley, “Vbpr: visual bayesian personalized ranking from implicit feedback,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 30, no. 1, 2016
2016
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F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , 2016, pp. 353–362
2016
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B. Li, Y. Wang, A. Singh, and Y. Vorobeychik, “Data poisoning attacks on factorization-based collaborative filtering,” Advances in neural information processing systems , vol. 29, pp. 1885–1893, 2016
2016
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X. He, H. Zhang, M.-Y. Kan, and T.-S. Chua, “Fast matrix factorization for online recommendation with implicit feedback,” in Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval , 2016, pp. 549–558
2016
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Y. Wu, C. DuBois, A. X. Zheng, and M. Ester, “Collaborative denoising auto-encoders for top-n recommender systems,” in Proceedings of the ninth ACM international conference on web search and data mining , 2016, pp. 153–162
2016
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W. Zhou, J. Wen, Q. Xiong, M. Gao, and J. Zeng, “Svm-tia a shilling attack detection method based on svm and target item analysis in recommender systems,” Neurocomputing , vol. 210, pp. 197–205, 2016
2016
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X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proceedings of the 26th international conference on world wide web , 2017, pp. 173–182
2017
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2017
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2017
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S. Kapoor, V. Kapoor, and R. Kumar, “A review of attacks and its detection attributes on collaborative recommender systems.” International Journal of Advanced Research in Computer Science , vol. 8, no. 7, 2017
2017
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X. He, Z. He, J. Song, Z. Liu, Y. Jiang, and T. Chua, “NAIS: neural attentive item similarity model for recommendation,” TKDE , vol. 30, no. 12, pp. 2354–2366, 2018
2018
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J. Wang, P. Huang, H. Zhao, Z. Zhang, B. Zhao, and D. L. Lee, “Billion-scale commodity embedding for e-commerce recommendation in alibaba,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018, pp. 839–848
2018
Cited alongside, same era.
H. Wang, F. Zhang, X. Xie, and M. Guo, “Dkn: Deep knowledge-aware network for news recommendation,” in Proceedings of the 2018 world wide web conference , 2018, pp. 1835–1844
2018
Cited alongside, same era.
X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “Lightgcn: Simplifying and powering graph convolution network for recommendation,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , 2020, pp. 639–648
2020
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2020
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K. Muhammad, Q. Wang, D. O’Reilly-Morgan, E. Tragos, B. Smyth, N. Hurley, J. Geraci, and A. Lawlor, “Fedfast: Going beyond average for faster training of federated recommender systems,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 1234–1242
2020
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E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2020, pp. 2938–2948
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G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan, X. Xie, and Z. Li, “Drn: A deep reinforcement learning framework for news recommendation,” in Proceedings of the 2018 World Wide Web Conference , 2018, pp. 167–176
2018
Cited alongside, same era.
M. Fang, G. Yang, N. Z. Gong, and J. Liu, “Poisoning attacks to graph-based recommender systems,” in Proceedings of the 34th Annual Computer Security Applications Conference , 2018, pp. 381–392
2018
Cited alongside, same era.
G. D. P. Regulation, “General data protection regulation (gdpr),” Intersoft Consulting, Accessed in October , vol. 24, no. 1, 2018
2018
Cited alongside, same era.
X. He, X. Du, X. Wang, F. Tian, J. Tang, and T. Chua, “Outer product-based neural collaborative filtering,” in IJCAI . ijcai.org, 2018, pp. 2227–2233
2018
Cited alongside, same era.
2018
Cited alongside, same era.
X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural graph collaborative filtering,” in Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval , 2019, pp. 165–174
2019
Cited alongside, same era.
Y. Wei, X. Wang, L. Nie, X. He, R. Hong, and T.-S. Chua, “Mmgcn: Multi-modal graph convolution network for personalized recommendation of micro-video,” in Proceedings of the 27th ACM International Conference on Multimedia , 2019, pp. 1437–1445
2019
Cited alongside, same era.
Y. Wei, Z. Cheng, X. Yu, Z. Zhao, L. Zhu, and L. Nie, “Personalized hashtag recommendation for micro-videos,” in Proceedings of the 27th ACM International Conference on Multimedia , 2019, pp. 1446–1454
2019
Cited alongside, same era.
2020
Later among the works it cites.
M. Fang, X. Cao, J. Jia, and N. Gong, “Local model poisoning attacks to byzantine-robust federated learning,” in 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20) , 2020, pp. 1605–1622
2020
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M. Fang, N. Z. Gong, and J. Liu, “Influence function based data poisoning attacks to top-n recommender systems,” in Proceedings of The Web Conference 2020 , 2020, pp. 3019–3025
2020
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K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. Quek, and H. V. Poor, “Federated learning with differential privacy: Algorithms and performance analysis,” IEEE Transactions on Information Forensics and Security , vol. 15, pp. 3454–3469, 2020
2020
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W. Krichene and S. Rendle, “On sampled metrics for item recommendation,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 1748–1757
2020
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X. Wang, T. Huang, D. Wang, Y. Yuan, Z. Liu, X. He, and T. Chua, “Learning intents behind interactions with knowledge graph for recommendation,” in WWW , 2021, pp. 878–887
2021
Later among the works it cites.
Z. Liu, P. Qian, X. Wang, Y. Zhuang, L. Qiu, and X. Wang, “Combining graph neural networks with expert knowledge for smart contract vulnerability detection,” IEEE Transactions on Knowledge and Data Engineering , 2021
2021
Later among the works it cites.
H. Huang, J. Mu, N. Z. Gong, Q. Li, B. Liu, and M. Xu, “Data poisoning attacks to deep learning based recommender systems,” in NDSS . The Internet Society, 2021
2021
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P. S. Chauhan and N. Kshetri, “2021 state of the practice in data privacy and security,” Computer , vol. 54, no. 08, pp. 125–132, 2021
2021
Later among the works it cites.
F. Liang, W. Pan, and Z. Ming, “Fedrec++: Lossless federated recommendation with explicit feedback,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 5, 2021, pp. 4224–4231
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Wu, Q. Liu, Z. Huang, Y. Ning, H. Wang, E. Chen, J. Yi, and B. Zhou, “Hierarchical personalized federated learning for user modeling,” in Proceedings of the Web Conference 2021 , 2021, pp. 957–968
2021
Later among the works it cites.
L. Minto, M. Haller, B. Livshits, and H. Haddadi, “Stronger privacy for federated collaborative filtering with implicit feedback,” in Fifteenth ACM Conference on Recommender Systems , 2021, pp. 342–350
2021
Later among the works it cites.
S. Zhang, H. Yin, T. Chen, Z. Huang, Q. V. H. Nguyen, and L. Cui, “Pipattack: Poisoning federated recommender systems for manipulating item promotion,” in WSDM . ACM, 2022, pp. 1415–1423
2022
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