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Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions.
BPR: Bayesian personalized ranking from implicit feedback. In
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Deep collaborative filtering via marginalized denoising auto-encoder. In
Sheng Li, Jaya Kawale, and Yun Fu. 2015 · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics. In
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
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Improved variational inference with inverse autoregressive flow. In
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. 2016 · 2016
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Collaborative denoising auto-encoders for top-n recommender systems. In
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016 · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework. In
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017 · 2017
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Collaborative Variational Autoencoder for Recommender Systems. In
Xiaopeng Li and James She. 2017 · 2017
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Recommendation with social relationships via deep learning. In
Dimitrios Rafailidis and Fabio Crestani. 2017 · 2017
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Irgan: A minimax game for unifying generative and discriminative information retrieval models. In
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017 · 2017
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Autosvd++ an efficient hybrid collaborative filtering model via contractive auto-encoders. In
Shuai Zhang, Lina Yao, and Xiwei Xu. 2017 · 2017
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Adversarial personalized ranking for recommendation. In
Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018 · 2018
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Variational Autoencoders for Collaborative Filtering. In
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018 · 2018
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Learning Disentangled Representations for Recommendation. In
Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019 · 2019
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VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders.. In
Xianwen Yu, Xiaoning Zhang, Yang Cao, and Min Xia. 2019 · 2019
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IPGAN: Generating informative item pairs by adversarial sampling
Guibing Guo, Huan Zhou, Bowei Chen, Zhirong Liu, Xiao Xu, Xu Chen, Zhenhua Dong, and Xiuqiang He. 2020 · 2020
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Lightgcn: Simplifying and powering graph convolution network for recommendation. In
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
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Denoising diffusion probabilistic models. In
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
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Sampling-decomposable generative adversarial recommender. In
Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, and Enhong Chen. 2020 · 2020
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Sequential recommendation with self-attentive multi-adversarial network. In
Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin Ding, and Ji-Rong Wen. 2020 · 2020
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Unbiased Learning for the Causal Effect of Recommendation. In
Masahiro Sato, Sho Takemori, Janmajay Singh, and Tomoko Ohkuma. 2020 · 2020
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Recvae: A new variational autoencoder for top-n recommendations with implicit feedback. In
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, and Chun-Yi Lee. 2022 · 2022
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Generative Adversarial Framework for Cold-Start Item Recommendation. In
Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He, and Zhoujun Li. 2022 · 2022
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Diffusion models in vision: A survey
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. 2022 · 2022
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Prodiff: Progressive fast diffusion model for high-quality text-to-speech. In
Rongjie Huang, Zhou Zhao, Huadai Liu, Jinglin Liu, Chenye Cui, and Yi Ren. 2022 · 2022
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Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I Nikolenko. 2020 · 2020
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Structured denoising diffusion models in discrete state-spaces. In
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg. 2021 · 2021
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Recommender systems based on generative adversarial networks: A problem-driven perspective
Min Gao, Junwei Zhang, Junliang Yu, Jundong Li, Junhao Wen, and Qingyu Xiong. 2021 · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions. In
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling. 2021 · 2021
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Bilateral denoising diffusion models
Max WY Lam, Jun Wang, Rongjie Huang, Dan Su, and Dong Yu. 2021 · 2021
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Improved denoising diffusion probabilistic models. In
Alexander Quinn Nichol and Prafulla Dhariwal. 2021 · 2021
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Grad-tts: A diffusion probabilistic model for text-to-speech. In
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov. 2021 · 2021
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Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B Hashimoto. 2022 · 2022
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Understanding diffusion models: A unified perspective. In
Calvin Luo. 2022 · 2022
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Variational Reasoning about User Preferences for Conversational Recommendation. In
Zhaochun Ren, Zhi Tian, Dongdong Li, Pengjie Ren, Liu Yang, Xin Xin, Huasheng Liang, Maarten de Rijke, and Zhumin Chen. 2022 · 2022
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High-resolution image synthesis with latent diffusion models. In
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
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Recommendation via Collaborative Diffusion Generative Model. In
Joojo Walker, Ting Zhong, Fengli Zhang, Qiang Gao, and Fan Zhou. 2022 · 2022
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Causal Inference for Knowledge Graph based Recommendation
Yinwei Wei, Xiang Wang, Liqiang Nie, Shaoyu Li, Dingxian Wang, and Tat-Seng Chua. 2022 · 2022
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DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, and Meng Wang. 2022 · 2022
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Negative Sampling for Contrastive Representation Learning: A Review
Lanling Xu, Jianxun Lian, Wayne Xin Zhao, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, and Ji-Rong Wen. 2022 · 2022
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More control for free! image synthesis with semantic diffusion guidance. In
Xihui Liu, Dong Huk Park, Samaneh Azadi, Gong Zhang, Arman Chopikyan, Yuxiao Hu, Humphrey Shi, Anna Rohrbach, and Trevor Darrell. 2023 · 2023
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Learning Robust Recommenders through Cross-Model Agreement. In
Yu Wang, Xin Xin, Zaiqiao Meng, Joemon M Jose, Fuli Feng, and Xiangnan He. 2022b · 2025
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