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Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS).
Assessing the impact of a user-item collaborative attack on class of users
Yashar Deldjoo, T. D. Noia, and Felice Antonio Merra. 2019 · 1908
Earlier work this paper cites.
Amazon.com recommendations: Item-to-item collaborative filtering
Greg Linden, Brent Smith, and Jeremy York. 2003 · 2003
Earlier work this paper cites.
Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2004
Earlier work this paper cites.
Limited knowledge shilling attacks in collaborative filtering systems
Robin Burke, Bamshad Mobasher, and Runa Bhaumik. 2005a · 2005
Earlier work this paper cites.
Segment-based injection attacks against collaborative filtering recommender systems
Robin Burke, Bamshad Mobasher, Runa Bhaumik, and Chad Williams. 2005b · 2005
Earlier work this paper cites.
Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015 · 2015
Earlier work this paper cites.
Shilling attack models in recommender system
Parneet Kaur and Shivani Goel. 2016 · 2016
Earlier work this paper cites.
Data poisoning attacks on factorization-based collaborative filtering
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik. 2016 · 2016
Earlier work this paper cites.
Poisoning attacks to graph-based recommender systems
Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, and Jia Liu. 2018 · 2018
Earlier work this paper cites.
Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi. 2018 · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Mmgcn: Multi-modal graph convolution network for personalized recommendation of micro-video
Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019 · 2019
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Influence function based data poisoning attacks to top-n recommender systems
Minghong Fang, Neil Zhenqiang Gong, and Jia Liu. 2020 · 2020
Earlier work this paper cites.
Is bert really robust? a strong baseline for natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
Earlier work this paper cites.
Bert-attack: Adversarial attack against bert using bert
Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, and Xipeng Qiu. 2020 · 2020
Earlier work this paper cites.
Attacking recommender systems with augmented user profiles
Chen Lin, Si Chen, Hui Li, Yanghua Xiao, Lianyun Li, and Qian Yang. 2020 · 2020
Earlier work this paper cites.
TextAttack: A framework for adversarial attacks, data augmentation, and adversarial training in NLP
John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020 · 2020
Cited alongside, same era.
Poisonrec: An adaptive data poisoning framework for attacking black-box recommender systems
Junshuai Song, Zhao Li, Zehong Hu, Yucheng Wu, Zhenpeng Li, Jian Li, and Jun Gao. 2020 · 2020
Cited alongside, same era.
MIND: A large-scale dataset for news recommendation
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, and Ming Zhou. 2020 · 2020
Cited alongside, same era.
Practical data poisoning attack against next-item recommendation
Hengtong Zhang, Yaliang Li, Bolin Ding, and Jing Gao. 2020 · 2020
Cited alongside, same era.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
Cited alongside, same era.
Do llms understand user preferences? evaluating llms on user rating prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed H. Chi, and Derek Zhiyuan Cheng. 2023 · 2023
Later among the works it cites.
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 · 2023
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Is chatgpt a good recommender? a preliminary study
Junling Liu, Chaoyong Liu, Renjie Lv, Kangdi Zhou, and Yan Bin Zhang. 2023 · 2023
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Large language model augmented narrative driven recommendations
Sheshera Mysore, Andrew McCallum, and Hamed Zamani. 2023 · 2023
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Hai Huang, Jiaming Mu, Neil Zhenqiang Gong, Qi Li, Bin Liu, and Mingwei Xu. 2021 · 2021
Cited alongside, same era.
Mining latent structures for multimedia recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, and Liang Wang. 2021a · 2021
Cited alongside, same era.
Causal intervention for leveraging popularity bias in recommendation
Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021b · 2021
Cited alongside, same era.
M6-rec: Generative pretrained language models are open-ended recommender systems
Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022 · 2022
Cited alongside, same era.
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 unified conversational recommender systems via knowledge-enhanced prompt learning
Xiaolei Wang, Kun Zhou, Ji rong Wen, and Wayne Xin Zhao. 2022 · 2022
Cited alongside, same era.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Tensor trust: Interpretable prompt injection attacks from an online game
Sam Toyer, Olivia Watkins, Ethan Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, and Stuart Russell. 2023 · 2023
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Recad: Towards a unified library for recommender attack and defense
Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, and Xiangnan He. 2023a · 2023
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Prediction of tumor lymph node metastasis using wasserstein distance-based generative adversarial networks combing with neural architecture search for predicting
Yawen Wang and Shihua Zhang. 2023 · 2023
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Collaborative word-based pre-trained item representation for transferable recommendation
Shenghao Yang, Chenyang Wang, Yankai Liu, Kangping Xu, Weizhi Ma, Yiqun Liu, Min Zhang, Haitao Zeng, Junlan Feng, and Chao Deng. 2023 · 2023
Later among the works it cites.
Where to go next for recommender systems? id- vs. modality-based recommender models revisited
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023 · 2023
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Mining stable preferences: Adaptive modality decorrelation for multimedia recommendation
Jinghao Zhang, Qiang Liu, Shu Wu, and Liang Wang. 2023a · 2023
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PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Neil Zhenqiang Gong, Yue Zhang, and Xing Xie. 2023 · 2023
Later among the works it cites.
A survey of generative search and recommendation in the era of large language models
Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2024 · 2024
Closest in time.
Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles
Chen Lin, Si Chen, Meifang Zeng, Sheng Zhang, Min Gao, and Hui Li. 2024 · 2024
Closest in time.
Uplift modeling for target user attacks on recommender systems
Wenjie Wang, Changsheng Wang, Fuli Feng, Wentao Shi, Daizong Ding, and Tat-Seng Chua. 2024b · 2024
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