Fetching the paper…
Reading the bibliography…
Recommendation algorithms rely on user historical interactions to deliver personalized suggestions, which raises significant privacy concerns.
Spectral graph theory . Vol. 92
Fan RK Chung. 1997 · 1997
Earlier work this paper cites.
An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval . 230–237
Jonathan L Herlocker, Joseph A Konstan, Al Borchers, and John Riedl. 1999 · 1999
Earlier work this paper cites.
The trec-8 question answering track report.. In Trec , Vol. 99. 77–82
Ellen M Voorhees et al · 1999
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web . 285–295
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Cumulated gain-based evaluation of IR techniques
Kalervo Järvelin and Jaana Kekäläinen. 2002 · 2002
Earlier work this paper cites.
Evaluating collaborative filtering recommender systems
Jonathan L Herlocker, Joseph A Konstan, Loren G Terveen, and John T Riedl. 2004 · 2004
Earlier work this paper cites.
Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web . 22–32
Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. 2005 · 2005
Earlier work this paper cites.
Spectral graph theory and its applications. In 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS’07) . IEEE, 29–38
Daniel A Spielman. 2007 · 2007
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining . Ieee, 263–272
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008 · 2008
Earlier work this paper cites.
K-means clustering of proportional data using L1 distance. In 2008 19th international conference on pattern recognition . IEEE, 1–4
Hisashi Kashima, Jianying Hu, Bonnie Ray, and Moninder Singh. 2008 · 2008
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Music recommendation and the long tail. In Proceedings of the workshop on music recommendation and discovery (WOMRAD) . 55–58
Mark Levy and Klaas Bosteels. 2010 · 2010
Earlier work this paper cites.
Robust distance metric learning via simultaneous l1-norm minimization and maximization. In International conference on machine learning . PMLR, 1836–1844
Hua Wang, Feiping Nie, and Heng Huang. 2014 · 2014
Earlier work this paper cites.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
Earlier work this paper cites.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web . 507–517
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics . PMLR, 1273–1282
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
Cited alongside, same era.
L1-Norm distance minimization-based fast robust twin support vector k k -plane clustering
Qiaolin Ye, Henghao Zhao, Zechao Li, Xubing Yang, Shangbing Gao, Tongming Yin, and Ning Ye. 2017 · 2017
Cited alongside, same era.
General data protection regulation (GDPR)
EU. 2018 · 2018
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019 · 2019
How powerful is graph convolution for recommendation?. In Proceedings of the 30th ACM international conference on information & knowledge management . 1619–1629
Yifei Shen, Yongji Wu, Yao Zhang, Caihua Shan, Jun Zhang, B Khaled Letaief, and Dongsheng Li. 2021 · 2021
Later among the works it cites.
Parameter-free dynamic graph embedding for link prediction
Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, and Ning Gu. 2022 · 2022
Later among the works it cites.
Federated neural collaborative filtering
Vasileios Perifanis and Pavlos S Efraimidis. 2022 · 2022
Later among the works it cites.
FIRE: Fast incremental recommendation with graph signal processing. In Proceedings of the ACM Web Conference 2022 . 2360–2369
Jiafeng Xia, Dongsheng Li, Hansu Gu, Jiahao Liu, Tun Lu, and Ning Gu. 2022b · 2022
Later among the works it cites.
Recommendation unlearning via matrix correction
Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Jiongran Wu, Peng Zhang, Li Shang, and Ning Gu. 2023a · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang. 2019 · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019 · 2019
Cited alongside, same era.
Secure federated matrix factorization
Di Chai, Leye Wang, Kai Chen, and Qiang Yang. 2020 · 2020
Cited alongside, same era.
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 . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Cited alongside, same era.
A review of applications in federated learning
Li Li, Yuxi Fan, Mike Tse, and Kuo-Yi Lin. 2020a · 2020
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020b · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020c · 2020
Cited alongside, same era.
Later among the works it cites.
Personalized graph signal processing for collaborative filtering. In Proceedings of the ACM Web Conference 2023 . 1264–1272
Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, and Ning Gu. 2023b · 2023
Later among the works it cites.
Heterogeneous federated learning: State-of-the-art and research challenges
Mang Ye, Xiuwen Fang, Bo Du, Pong C Yuen, and Dacheng Tao. 2023 · 2023
Later among the works it cites.
Dual personalization on federated recommendation. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence . 4558–4566
Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijian Zhang, Chengqi Zhang, and Bo Yang. 2023 · 2023
Later among the works it cites.
Recommender Systems: Frontiers and Practices
Dongsheng Li, Jianxun Lian, Le Zhang, Kan Ren, Tun Lu, Tao Wu, and Xing Xie. 2024 · 2024
Later among the works it cites.
Filtering Discomforting Recommendations with Large Language Models
Jiahao Liu, Yiyang Shao, Peng Zhang, Dongsheng Li, Hansu Gu, Chao Chen, Longzhi Du, Tun Lu, and Ning Gu. 2024 · 2024
Later among the works it cites.
A survey on federated recommendation systems
Zehua Sun, Yonghui Xu, Yong Liu, Wei He, Lanju Kong, Fangzhao Wu, Yali Jiang, and Lizhen Cui. 2024 · 2024
Later among the works it cites.
Hierarchical Graph Signal Processing for Collaborative Filtering. In Proceedings of the ACM on Web Conference 2024 . 3229–3240
Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, and Ning Gu. 2024a · 2024
Later among the works it cites.
GPFedRec: Graph-Guided Personalization for Federated Recommendation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4131–4142
Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang, Peng Yan, and Bo Yang. 2024 · 2024
Later among the works it cites.
Enhancing Cross-Domain Recommendations with Memory-Optimized LLM-Based User Agents
Jiahao Liu, Shengkang Gu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, and Ning Gu. 2025a · 2025
Closest in time.
Mitigating Popularity Bias in Collaborative Filtering through Fair Sampling
Jiahao Liu, Dongsheng Li, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, and Ning Gu. 2025b · 2025
Closest in time.
Enhancing LLM-Based Recommendations Through Personalized Reasoning
Jiahao Liu, Xueshuo Yan, Dongsheng Li, Guangping Zhang, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, and Ning Gu. 2025c · 2025
Closest in time.