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Recommender systems predict personalized item rankings based on user preference distributions derived from historical behavior data.
The “independent components” of natural scenes are edge filters
Anthony J Bell and Terrence J Sejnowski · 1997
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On bias, variance, 0/1-loss, and the curse-of-dimensionality
Jerome H. Friedman · 1997
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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BPR: bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme · 2009
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Kernel density estimation via diffusion
Zdravko I Botev, Joseph F Grotowski, and Dirk P Kroese · 2010
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian J. McAuley · 2016
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Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian J. McAuley · 2018
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Personalized top-n sequential recommendation via convolutional sequence embedding
Jiaxi Tang and Ke Wang · 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 · 2019
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Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
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Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian J. McAuley · 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
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Sequential recommender systems: Challenges, progress and prospects
Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, and Mehmet A. Orgun · 2019
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Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations
Hui Fang, Danning Zhang, Yiheng Shu, and Guibing Guo · 2020
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Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Automated embedding size search in deep recommender systems
Haochen Liu, Xiangyu Zhao, Chong Wang, Xiaobing Liu, and Jiliang Tang · 2020
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Unbiased learning for the causal effect of recommendation
Masahiro Sato, Sho Takemori, Janmajay Singh, and Tomoko Ohkuma · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
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Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, and Hao Wang · 2021
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Lighter and better: Low-rank decomposed self-attention networks for next-item recommendation
Xinyan Fan, Zheng Liu, Jianxun Lian, Wayne Xin Zhao, Xing Xie, and Ji-Rong Wen · 2021
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Cross-domain recommendation: Challenges, progress, and prospects
Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu · 2021
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Cache-augmented inbatch importance resampling for training recommender retriever
Jin Chen, Defu Lian, Yucheng Li, Baoyun Wang, Kai Zheng, and Enhong Chen · 2022
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Latent diffusion for language generation
Justin Lovelace, Varsha Kishore, Chao Wan, Eliot Shekhtman, and Kilian Q. Weinberger · 2023
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Continuous input embedding size search for recommender systems
Yunke Qu, Tong Chen, Xiangyu Zhao, Lizhen Cui, Kai Zheng, and Hongzhi Yin · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning, Stefano Ermon, and Chelsea Finn · 2023
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Recommender systems with generative retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, and Mahesh Sathiamoorthy · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurélien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
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Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5)
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Classifier-free diffusion guidance
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Understanding diffusion models: A unified perspective
Calvin Luo · 2022
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Text and code embeddings by contrastive pre-training
Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav Shyam, Boris Power, Tyna Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe Petroski Such, Kenny Hsu, Madeleine Thompson, Tabarak Khan, Toki Sherbakov, Joanne Jang, Peter Welinder, and Lilian Weng · 2022
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B. Hall, Daniel Cer, and Yinfei Yang · 2022
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Item recommendation from implicit feedback
Steffen Rendle · 2022
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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
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Revisiting neural retrieval on accelerators
Jiaqi Zhai, Zhaojie Gong, Yueming Wang, Xiao Sun, Zheng Yan, Fu Li, and Xing Liu · 2023
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Empowering collaborative filtering with principled adversarial contrastive loss
An Zhang, Leheng Sheng, Zhibo Cai, Xiang Wang, and Tat-Seng Chua · 2023
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On softmax direct preference optimization for recommendation
Yuxin Chen, Junfei Tan, An Zhang, Zhengyi Yang, Leheng Sheng, Enzhi Zhang, Xiang Wang, and Tat-Seng Chua · 2024
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On the embedding collapse when scaling up recommendation models
Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, and Mingsheng Long · 2024
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Diffurec: A diffusion model for sequential recommendation
Zihao Li, Aixin Sun, and Chenliang Li · 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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A survey on diffusion models for recommender systems
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Inductive cognitive diagnosis for fast student learning in web-based intelligent education systems
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Simpo: Simple preference optimization with a reference-free reward
Yu Meng, Mengzhou Xia, and Danqi Chen · 2024
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ORCDF: An oversmoothing-resistant cognitive diagnosis framework for student learning in online education systems
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Representation learning with large language models for recommendation
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Language models encode collaborative signals in recommendation
Leheng Sheng, An Zhang, Yi Zhang, Yuxin Chen, Xiang Wang, and Tat-Seng Chua · 2024
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Diffusion model alignment using direct preference optimization
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Diffusion models: A comprehensive survey of methods and applications
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Actions speak louder than words: Trillion-parameter sequential transducers for generative recommendations
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Robust collaborative filtering to popularity distribution shift
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Denoising diffusion recommender model
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Graph signal diffusion model for collaborative filtering
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