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Federated Recommendation (FR) emerges as a novel paradigm that enables privacy-preserving recommendations.
Federated collaborative filtering for privacy-preserving personalized recommendation system
Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman A Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan. 2019 · 1901
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On the solution of ill-posed problems and the method of regularization
Andrei Nikolaevich Tikhonov. 1963 · 1963
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Autoregressive entity retrieval
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2020 · 2010
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
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Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015 · 2015
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley. 2016 · 2016
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
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Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 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.
Fedrec: Federated recommendation with explicit feedback
Guanyu Lin, Feng Liang, Weike Pan, and Zhong Ming. 2020 · 2020
Cited alongside, same era.
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Cited alongside, same era.
Towards universal sequence representation learning for recommender systems
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. 2022 · 2022
Cited alongside, same era.
Towards communication efficient and fair federated personalized sequential recommendation
Defending substitution-based profile pollution attacks on sequential recommenders
Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, and Dong Wang. 2022 · 2022
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Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
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Palr: Personalization aware llms for recommendation
Zheng Chen. 2023 · 2023
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Leveraging large language models for sequential recommendation
Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, and Marios Fragkoulis. 2023 · 2023
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Large language models as zero-shot conversational recommenders
Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. 2023 · 2023
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Sichun Luo, Yuanzhang Xiao, Yang Liu, Congduan Li, and Linqi Song. 2022 · 2022
Cited alongside, same era.
Zero-shot recommendation as language modeling
Damien Sileo, Wout Vossen, and Robbe Raymaekers. 2022 · 2022
Cited alongside, same era.
Transformer memory as a differentiable search index
Yi Tay, Vinh Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, et al. 2022 · 2022
Cited alongside, same era.
Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022
Cited alongside, same era.
Fedcl: Federated contrastive learning for privacy-preserving recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, and Xing Xie. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot rankers for recommender systems
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2023b
Cited in the paper.
Text is all you need: Learning language representations for sequential recommendation
Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, and Julian McAuley. 2023a
Cited in the paper.
Later among the works it cites.
Learning vector-quantized item representation for transferable sequential recommenders
Yupeng Hou, Zhankui He, Julian McAuley, and Wayne Xin Zhao. 2023a · 2023
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Do llms understand user preferences? evaluating llms on user rating prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023 · 2023
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Is chatgpt good at search? investigating large language models as re-ranking agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023 · 2023
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Recommendation as instruction following: A large language model empowered recommendation approach
Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023 · 2023
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Transfr: Transferable federated recommendation with pre-trained language models
Honglei Zhang, He Liu, Haoxuan Li, and Yidong Li. 2024 · 2024
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