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Recent advances in Foundation Models such as Large Language Models (LLMs) have propelled them to the forefront of Recommender Systems (RS).
Language models are few-shot learners
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Null it out: Guarding protected attributes by iterative nullspace projection
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Adversarial learning
Daniel Lowd and Christopher Meek. 2005 · 2005
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Probabilistic matrix factorization
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
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Recommender systems survey
Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. 2013 · 2013
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
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Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
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Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. 2016 · 2016
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A dynamic recurrent model for next basket recommendation
Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016 · 2016
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Recurrent recommender networks
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. 2017 · 2017
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Joint representation learning for top-n recommendation with heterogeneous information sources
Yongfeng Zhang, Qingyao Ai, Xu Chen, and W Bruce Croft. 2017 · 2017
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Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Recurrent neural networks with top-k gains for session-based recommendations
Balázs Hidasi and Alexandros Karatzoglou. 2018 · 2018
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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
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User fairness in recommender systems
Jurek Leonhardt, Avishek Anand, and Megha Khosla. 2018 · 2018
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Trsdl: Tag-aware recommender system based on deep learning–intelligent computing systems
Nan Liang, Hai-Tao Zheng, Jin-Yuan Chen, Arun Kumar Sangaiah, and Cong-Zhi Zhao. 2018 · 2018
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The cost of fairness in binary classification
Aditya Krishna Menon and Robert C Williamson. 2018 · 2018
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Fairness in algorithmic decision-making: Applications in multi-winner voting, machine learning, and recommender systems
Yash Raj Shrestha and Yongjie Yang. 2019 · 2019
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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
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Deep matrix factorization with implicit feedback embedding for recommendation system
Baolin Yi, Xiaoxuan Shen, Hai Liu, Zhaoli Zhang, Wei Zhang, Sannyuya Liu, and Naixue Xiong. 2019 · 2019
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The connection between popularity bias, calibration, and fairness in recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2020 · 2020
Cited alongside, same era.
Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. 2022 · 2022
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Experiments on generalizability of user-oriented fairness in recommender systems
Hossein A Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, and Mohammad Aliannejadi. 2022 · 2022
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Selective fairness in recommendation via prompts
Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Ao Xiang, Xu Zhang, Leyu Lin, and Qing He. 2022 · 2022
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Adversarial training methods for deep learning: A systematic review
Weimin Zhao, Sanaa Alwidian, and Qusay H Mahmoud. 2022 · 2022
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A unifying and general account of fairness measurement in recommender systems
Enrique Amigó, Yashar Deldjoo, Stefano Mizzaro, and Alejandro Bellogín. 2023 · 2023
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Counterfactual learning for recommender system
Zhenhua Dong, Hong Zhu, Pengxiang Cheng, Xinhua Feng, Guohao Cai, Xiuqiang He, Jun Xu, and Jirong Wen. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. 2020 · 2020
Cited alongside, same era.
Autoregressive entity retrieval
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2021 · 2021
Cited alongside, same era.
A flexible framework for evaluating user and item fairness in recommender systems
Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, and Tommaso Di Noia. 2021 · 2021
Cited alongside, same era.
Towards long-term fairness in recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, et al. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
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When large language models meet personalization: Perspectives of challenges and opportunities
Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, et al. 2023 · 2023
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Recommender systems in the era of large language models (llms)
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023 · 2023
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Openllama: An open reproduction of llama
Xinyang Geng and Hao Liu. 2023 · 2023
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How to Index Item IDs for Recommendation Foundation Models
Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2023 · 2023
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Yunqi Li and Yongfeng Zhang. 2023 · 2023
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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, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al. 2023 · 2023
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Peng Liu, Lemei Zhang, and Jon Atle Gulla. 2023 · 2023
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Safety assessment of chinese large language models
Hao Sun, Zhexin Zhang, Jiawen Deng, Jiale Cheng, and Minlie Huang. 2023 · 2023
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Llama: Open and efficient foundation language models
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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A survey on the fairness of recommender systems
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023 · 2023
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Openp5: Benchmarking foundation models for recommendation
Shuyuan Xu, Wenyue Hua, and Yongfeng Zhang. 2023 · 2023
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Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation
Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
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Exploring ai ethics of chatgpt: A diagnostic analysis
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. 2023 · 2023
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