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Recently, Large Language Model (LLM)-empowered recommender systems have revolutionized personalized recommendation frameworks and attracted extensive attention.
Application of majority voting to pattern recognition: an analysis of its behavior and performance
Louisa Lam and SY Suen · 1997
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Amazon. com recommendations: Item-to-item collaborative filtering
Greg Linden, Brent Smith, and Jeremy York · 2003
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The youtube video recommendation system
James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, et al · 2010
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Beyond position bias: Examining result attractiveness as a source of presentation bias in clickthrough data
Yisong Yue, Rajan Patel, and Hein Roehrig · 2010
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Shilling attacks against recommender systems: a comprehensive survey
Ihsan Gunes, Cihan Kaleli, Alper Bilge, and Huseyin Polat · 2014
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The MovieLens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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Inferring networks of substitutable and complementary products
Julian McAuley, Rahul Pandey, and Jure Leskovec · 2015
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Deep neural networks for Youtube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
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Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence
Dawen Liang, Jaan Altosaar, Laurent Charlin, and David M Blei · 2016
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Generating and personalizing bundle recommendations on steam
Apurva Pathak, Kshitiz Gupta, and Julian McAuley · 2017
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Learning tree-based deep model for recommender systems
Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai · 2018
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An efficient recommendation generation using relevant jaccard similarity
Sujoy Bag, Sri Krishna Kumar, and Manoj Kumar Tiwari · 2019
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Deep social collaborative filtering
Wenqi Fan, Yao Ma, Dawei Yin, Jianping Wang, Jiliang Tang, and Qing Li · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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User-video co-attention network for personalized micro-video recommendation
Shang Liu, Zhenzhong Chen, Hongyi Liu, and Xinghai Hu · 2019
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Improving neural language modeling via adversarial training
Dilin Wang, Chengyue Gong, and Qiang Liu · 2019
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SeqVAT: Virtual adversarial training for semi-supervised sequence labeling
Luoxin Chen, Weitong Ruan, Xinyue Liu, and Jianhua Lu · 2020
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A graph neural network framework for social recommendations
Wenqi Fan, Yao Ma, Qing Li, Jianping Wang, Guoyong Cai, Jiliang Tang, and Dawei Yin · 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
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Adversarial training for large neural language models
Xiaodong Liu, Hao Cheng, Pengcheng He, Weizhu Chen, Yu Wang, Hoifung Poon, and Jianfeng Gao · 2020
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Finding the reviews on yelp that actually matter to me: Innovative approach of improving recommender systems
Yi Luo, Liang Rebecca Tang, Eojina Kim, and Xi Wang · 2020
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Billion-scale recommendation with heterogeneous side information at taobao
Andreas Pfadler, Huan Zhao, Jizhe Wang, Lifeng Wang, Pipei Huang, and Dik Lun Lee · 2020
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BPE-dropout: Simple and effective subword regularization
Ivan Provilkov, Dmitrii Emelianenko, and Elena Voita · 2020
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Shilling attacks against collaborative recommender systems: a review
Mingdan Si and Qingshan Li · 2020
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Ccnet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Édouard Grave · 2020
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Gcn-based user representation learning for unifying robust recommendation and fraudster detection
Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, and Lizhen Cui · 2020
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Recent advances in adversarial training for adversarial robustness
Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang · 2021
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Attacking black-box recommendations via copying cross-domain user profiles
Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jianping Wang, Jiliang Tang, and Qing Li · 2021
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Jailbreak and guard aligned language models with only few in-context demonstrations
Zeming Wei, Yifei Wang, and Yisen Wang · 2023
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Certified robustness for large language models with self-denoising
Zhen Zhang, Guanhua Zhang, Bairu Hou, Wenqi Fan, Qing Li, Sijia Liu, Yang Zhang, and Shiyu Chang · 2023
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Defending against alignment-breaking attacks via robustly aligned LLM
Bochuan Cao, Yuanpu Cao, Lu Lin, and Jinghui Chen · 2024
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Enhancing job recommendation through llm-based generative adversarial networks
Yingpeng Du, Di Luo, Rui Yan, Xiaopei Wang, Hongzhi Liu, Hengshu Zhu, Yang Song, and Jie Zhang · 2024
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A survey on RAG meeting LLMs: Towards retrieval-augmented large language models
Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li · 2024
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Token-aware virtual adversarial training in natural language understanding
Linyang Li and Xipeng Qiu · 2021
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Contrastive learning for cold-start recommendation
Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan Li, Xuanping Li, and Tat-Seng Chua · 2021
Cited alongside, same era.
Knowledge-enhanced black-box attacks for recommendations
Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, and Yihua Huang · 2022
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
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A survey on retrieval-augmented text generation
Huayang Li, Yixuan Su, Deng Cai, Yan Wang, and Lemao Liu · 2022
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Learning to denoise unreliable interactions for graph collaborative filtering
Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang, and Wayne Xin Zhao · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Development of a liver disease-specific large language model chat interface using retrieval augmented generation
Jin Ge, Steve Sun, Joseph Owens, Victor Galvez, Oksana Gologorskaya, Jennifer C Lai, Mark J Pletcher, and Ki Lai · 2024
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Amazon-M2: A multilingual multi-locale shopping session dataset for recommendation and text generation
Wei Jin, Haitao Mao, Zheng Li, Haoming Jiang, Chen Luo, Hongzhi Wen, Haoyu Han, Hanqing Lu, Zhengyang Wang, Ruirui Li, et al · 2024
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Alphafin: Benchmarking financial analysis with retrieval-augmented stock-chain framework
Xiang Li, Zhenyu Li, Chen Shi, Yong Xu, Qing Du, Mingkui Tan, Jun Huang, and Wei Lin · 2024
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LLaRA: Large language-recommendation assistant
Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, and Xiangnan He · 2024
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CheatAgent: Attacking llm-empowered recommender systems via llm agent
Liang-bo Ning, Shijie Wang, Wenqi Fan, Qing Li, Xin Xu, Hao Chen, and Feiran Huang · 2024
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The refinedweb dataset for falcon llm: Outperforming curated corpora with web data only
Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Hamza Alobeidli, Alessandro Cappelli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay · 2024
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Enhancing sequential recommenders with augmented knowledge from aligned large language models
Yankun Ren, Zhongde Chen, Xinxing Yang, Longfei Li, Cong Jiang, Lei Cheng, Bo Zhang, Linjian Mo, and Jun Zhou · 2024
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The art of defending: A systematic evaluation and analysis of LLM defense strategies on safety and over-defensiveness
Neeraj Varshney, Pavel Dolin, Agastya Seth, and Chitta Baral · 2024
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Llm4dsr: Leveraing large language model for denoising sequential recommendation
Bohao Wang, Feng Liu, Changwang Zhang, Jiawei Chen, Yudi Wu, Sheng Zhou, Xingyu Lou, Jun Wang, Yan Feng, Chun Chen, et al · 2024
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Efficient adversarial training in llms with continuous attacks
Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, and Leo Schwinn · 2024
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OpenP5: An open-source platform for developing, training, and evaluating llm-based recommender systems
Shuyuan Xu, Wenyue Hua, and Yongfeng Zhang · 2024
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A survey on large language model (LLM) security and privacy: The good, the bad, and the ugly
Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang · 2024
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Financial report chunking for effective retrieval augmented generation
Antonio Jimeno Yepes, Yao You, Jan Milczek, Sebastian Laverde, and Leah Li · 2024
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Recommender systems in the era of large language models
Zihuai Zhao, Wenqi Fan, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, et al · 2024
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Adapting large language models by integrating collaborative semantics for recommendation
Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ming Chen, and Ji-Rong Wen · 2024
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How can recommender systems benefit from large language models: A survey
Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong Liu, Chuhan Wu, Xiangyang Li, Chenxu Zhu, et al · 2025
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Crud-rag: A comprehensive chinese benchmark for retrieval-augmented generation of large language models
Yuanjie Lyu, Zhiyu Li, Simin Niu, Feiyu Xiong, Bo Tang, Wenjin Wang, Hao Wu, Huanyong Liu, Tong Xu, and Enhong Chen · 2025
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Liangbo Ning, Ziran Liang, Zhuohang Jiang, Haohao Qu, Yujuan Ding, Wenqi Fan, Xiao-yong Wei, Shanru Lin, Hui Liu, Philip S. Yu, and Qing Li · 2025
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Automated disentangled sequential recommendation with large language models
Xin Wang, Hong Chen, Zirui Pan, Yuwei Zhou, Chaoyu Guan, Lifeng Sun, and Wenwu Zhu · 2025
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