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Large Language Models (LLMs) have empowered generative recommendation systems through fine-tuning user behavior data.
Personalized ranking for non-uniformly sampled items
Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011 · 2011
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Communication-efficient learning of deep networks from decentralized data
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2017 · 2017
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Balanced neighborhoods for multi-sided fairness in recommendation
Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018 · 2018
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The european union general data protection regulation: what it is and what it means
Chris Jay Hoofnagle, Bart Van Der Sloot, and Frederik Zuiderveen Borgesius. 2019 · 2019
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Secure federated matrix factorization
Di Chai, Leye Wang, Kai Chen, and Qiang Yang. 2020 · 2020
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Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020 · 2020
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Fedfast: Going beyond average for faster training of federated recommender systems
Khalil Muhammad, Qinqin Wang, Diarmuid O’Reilly-Morgan, Elias Tragos, Barry Smyth, Neil Hurley, James Geraci, and Aonghus Lawlor. 2020 · 2020
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Curriculum disentangled recommendation with noisy multi-feedback
Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, and Wenwu Zhu. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. 2021 · 2021
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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
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A survey on federated recommendation systems
Zehua Sun, Yonghui Xu, Yong Liu, Wei He, Lanju Kong, Fangzhao Wu, Yali Jiang, and Lizhen Cui. 2022 · 2022
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Fast-adapting and privacy-preserving federated recommender system
Qinyong Wang, Hongzhi Yin, Tong Chen, Junliang Yu, Alexander Zhou, and Xiangliang Zhang. 2022 · 2022
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A federated graph neural network framework for privacy-preserving personalization
Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Tao Qi, Yongfeng Huang, and Xing Xie. 2022 · 2022
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Dpmf: Decentralized probabilistic matrix factorization for privacy-preserving recommendation
Xu Yang, Yuchuan Luo, Shaojing Fu, Ming Xu, and Yingwen Chen. 2022 · 2022
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Federated large language model: A position paper
Chaochao Chen, Xiaohua Feng, Jun Zhou, Jianwei Yin, and Xiaolin Zheng. 2023 · 2023
P-mmf: Provider max-min fairness re-ranking in recommender system
Chen Xu, Sirui Chen, Jun Xu, Weiran Shen, Xiao Zhang, Gang Wang, and Zhenhua Dong. 2023a · 2023
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Fedrich: Towards efficient federated learning for heterogeneous clients using heuristic scheduling
He Yang, Wei Xi, Zizhao Wang, Yuhao Shen, Xinyuan Ji, Cerui Sun, and Jizhong Zhao. 2023 · 2023
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Decentralized graph neural network for privacy-preserving recommendation
Xiaolin Zheng, Zhongyu Wang, Chaochao Chen, Jiashu Qian, and Yao Yang. 2023 · 2023
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Nrdl: Decentralized user preference learning for privacy-preserving next poi recommendation
Jingmin An, Guanyu Li, and Wei Jiang. 2024 · 2024
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Robust federated finetuning of foundation models via alternating minimization of lora
Shuangyi Chen, Yue Ju, Hardik Dalal, Zhongwen Zhu, and Ashish Khisti. 2024 · 2024
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Fate-llm: A industrial grade federated learning framework for large language models
Tao Fan, Yan Kang, Guoqiang Ma, Weijing Chen, Wenbin Wei, Lixin Fan, and Qiang Yang. 2023 · 2023
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Chat-rec: Towards interactive and explainable llms-augmented recommender system
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023 · 2023
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Language models represent space and time
Wes Gurnee and Max Tegmark. 2023 · 2023
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Refrs: Resource-efficient federated recommender system for dynamic and diversified user preferences
Mubashir Imran, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Alexander Zhou, and Kai Zheng. 2023 · 2023
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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. 2023 · 2023
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Privaterec: Differentially private model training and online serving for federated news recommendation
Ruixuan Liu, Yang Cao, Yanlin Wang, Lingjuan Lyu, Yun Chen, and Hong Chen. 2023 · 2023
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Semi-decentralized federated ego graph learning for recommendation
Liang Qu, Ningzhi Tang, Ruiqi Zheng, Quoc Viet Hung Nguyen, Zi Huang, Yuhui Shi, and Hongzhi Yin. 2023 · 2023
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Llm-guided multi-view hypergraph learning for human-centric explainable recommendation
Zhixuan Chu, Yan Wang, Qing Cui, Longfei Li, Wenqing Chen, Sheng Li, Zhan Qin, and Kui Ren. 2024 · 2024
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Bias and unfairness in information retrieval systems: New challenges in the llm era
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Data-efficient fine-tuning for llm-based recommendation
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Transfr: Transferable federated recommendation with pre-trained language models
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