Fetching the paper…
Reading the bibliography…
In real-world recommender systems, implicitly collected user feedback, while abundant, often includes noisy false-positive and false-negative interactions.
A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability
Donald J Schuirmann. 1987 · 1987
Earlier work this paper cites.
Linear discriminant analysis
Petros Xanthopoulos, Panos M Pardalos, Theodore B Trafalis, Petros Xanthopoulos, Panos M Pardalos, and Theodore B Trafalis. 2013 · 2013
Earlier work this paper cites.
Modeling user preferences in recommender systems: A classification framework for explicit and implicit user feedback
Gawesh Jawaheer, Peter Weller, and Patty Kostkova. 2014 · 2014
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks. In Proc. of ICLR
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Earlier work this paper cites.
Image-based recommendations on styles and substitutes. In Proc. of SIGIR
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015 · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics. In Proc. of ICML
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
Earlier work this paper cites.
Cross-domain action recognition via collective matrix factorization with graph Laplacian regularization
Jun Tang, Haiqun Jin, Shoubiao Tan, and Dong Liang. 2016 · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks. In Proc. of ICLR
Max Welling and Thomas N Kipf. 2016 · 2016
Earlier work this paper cites.
Collaborative denoising auto-encoders for top-n recommender systems. In Proc. of WSDM
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016 · 2016
Earlier work this paper cites.
Collaborative knowledge base embedding for recommender systems. In Proc. of SIGKDD
Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016 · 2016
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
A collective variational autoencoder for top-n recommendation with side information. In Proc. of DLRS
Yifan Chen and Maarten de Rijke. 2018 · 2018
Earlier work this paper cites.
Self-attentive sequential recommendation. In Proc. of ICDM
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
Variational autoencoders for collaborative filtering. In Proc. of WebConf
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018 · 2018
Earlier work this paper cites.
Diffusion improves graph learning. In Proc. of NeurIPS
Johannes Gasteiger, Stefan Weißenberger, and Stephan Günnemann. 2019 · 2019
Earlier work this paper cites.
Recommender systems with heterogeneous side information. In Proc. of WebConf
Tianqiao Liu, Zhiwei Wang, Jiliang Tang, Songfan Yang, Gale Yan Huang, and Zitao Liu. 2019 · 2019
Earlier work this paper cites.
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proc. of CIKM
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Earlier work this paper cites.
LightGCN: Simplifying and powering graph convolution network for recommendation. In Proc. of SIGIR
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models. In Proc. of NeurIPS
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Cited alongside, same era.
Neural anisotropy directions. In Proc. of NeurIPS
Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi, and Pascal Frossard. 2020 · 2020
Cited alongside, same era.
Recvae: A new variational autoencoder for top-n recommendations with implicit feedback. In Proc. of WSDM
Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I Nikolenko. 2020 · 2020
Cited alongside, same era.
Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma. 2020 · 2020
Cited alongside, same era.
Mind: A large-scale dataset for news recommendation. In Proc. of ACL
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, et al · 2020
Cited alongside, same era.
D4Explainer: In-distribution GNN explanations via discrete denoising diffusion
Jialin Chen, Shirley Wu, Abhijit Gupta, and Rex Ying. 2023 · 2023
Later among the works it cites.
Diffuser: efficient transformers with multi-hop attention diffusion for long sequences. In Proc. of AAAI
Aosong Feng, Irene Li, Yuang Jiang, and Rex Ying. 2023 · 2023
Later among the works it cites.
MUDiff: Unified diffusion for complete molecule generation
Chenqing Hua, Sitao Luan, Minkai Xu, Rex Ying, Jie Fu, Stefano Ermon, and Doina Precup. 2023 · 2023
Later among the works it cites.
Adaptive graph contrastive learning for recommendation. In Proc. of SIGKDD
Yangqin Jiang, Chao Huang, and Lianghao Huang. 2023 · 2023
Later among the works it cites.
Autoregressive diffusion model for graph generation. In Proc. of ICML
Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B Aditya Prakash, and Chao Zhang. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proc. of CIKM
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis. In Proc. of NeurIPS
Prafulla Dhariwal and Alexander Nichol. 2021 · 2021
Cited alongside, same era.
Graph neural pre-training for enhancing recommendations using side information
Zaiqiao Meng, Siwei Liu, Craig Macdonald, and Iadh Ounis. 2021 · 2021
Cited alongside, same era.
Are my deep learning systems fair? An empirical study of fixed-seed training. In Proc. of NeuIPS
Shangshu Qian, Viet Hung Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, and Sameena Shah. 2021 · 2021
Cited alongside, same era.
Self-supervised graph learning for recommendation. In Proc. of SIGIR
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021 · 2021
Cited alongside, same era.
Graph neural networks for recommender system. In Proc. of WSDM
Chen Gao, Xiang Wang, Xiangnan He, and Yong Li. 2022 · 2022
Cited alongside, same era.
Diffusion models for graphs benefit from discrete state spaces. In Proc. of LoG
Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin, and Roger Wattenhofer. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Graph transformer for recommendation
Chaoliu Li, Lianghao Xia, Xubin Ren, Yaowen Ye, Yong Xu, and Chao Huang. 2023b · 2023
Later among the works it cites.
DiffuRec: A Diffusion Model for Sequential Recommendation
Zihao Li, Aixin Sun, and Chenliang Li. 2023a · 2023
Later among the works it cites.
Diffusion augmentation for sequential recommendation. In Proc. of CIKM
Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, and Feng Tian. 2023 · 2023
Later among the works it cites.
DiGress: Discrete denoising diffusion for graph generation. In Proc. of ICLR
Clément Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard. 2023 · 2023
Later among the works it cites.
Diffusion Recommender Model
Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, and Tat-Seng Chua. 2023 · 2023
Later among the works it cites.
Difformer: Scalable (graph) transformers induced by energy constrained diffusion
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, and Junchi Yan. 2023 · 2023
Later among the works it cites.
Directional diffusion models for graph representation learning
Run Yang, Yuling Yang, Fan Zhou, and Qiang Sun. 2023 · 2023
Later among the works it cites.
Towards robust neural graph collaborative filtering via structure denoising and embedding perturbation
Haibo Ye, Xinjie Li, Yuan Yao, and Hanghang Tong. 2023 · 2023
Later among the works it cites.
Large multi-modal encoders for recommendation
Zixuan Yi, Zijun Long, Iadh Ounis, Craig Macdonald, and Richard Mccreadie. 2023a · 2023
Later among the works it cites.
Contrastive Graph Learning with Positional Representation for Recommendation
Zixuan Yi, Iadh Ounis, and Craig Macdonald. 2023b · 2023
Later among the works it cites.
Contrastive graph prompt-tuning for cross-domain recommendation
Zixuan Yi, Iadh Ounis, and Craig Macdonald. 2023c · 2023
Later among the works it cites.
SLED: Structure learning based denoising for recommendation
Shengyu Zhang, Tan Jiang, Kun Kuang, Fuli Feng, Jin Yu, Jianxin Ma, Zhou Zhao, Jianke Zhu, Hongxia Yang, Tat-Seng Chua, et al · 2023
Later among the works it cites.
Diffusion models in nlp: A survey
Hao Zou, Zae Myung Kim, and Dongyeop Kang. 2023 · 2023
Later among the works it cites.