Fetching the paper…
Reading the bibliography…
As its availability and generality in online services, implicit feedback is more commonly used in recommender systems.
Exponential and moment inequalities for U-statistics
Evarist Giné, Rafał Latała, and Joel Zinn. 2000 · 2000
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining . Ieee, 263–272
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008 · 2008
Earlier work this paper cites.
One-class collaborative filtering. In 2008 Eighth IEEE international conference on data mining . IEEE, 502–511
Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008 · 2008
Earlier work this paper cites.
Personalized ranking for non-uniformly sampled items. In Proceedings of KDD Cup 2011 . PMLR, 231–247
Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2012 · 2011
Earlier work this paper cites.
Challenging the empirical mean and empirical variance: a deviation study. In Annales de l’IHP Probabilités et statistiques , Vol. 48. 1148–1185
Olivier Catoni. 2012 · 2012
Earlier work this paper cites.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2012 · 2012
Earlier work this paper cites.
Knowledge-refined Denoising Network for Robust Recommendation
Xinjun Zhu, Yuntao Du, Yuren Mao, Lu Chen, Yujia Hu, and Yunjun Gao. 2023 · 2012
Earlier work this paper cites.
PU learning for matrix completion. In International conference on machine learning . PMLR, 2445–2453
Cho-Jui Hsieh, Nagarajan Natarajan, and Inderjit Dhillon. 2015 · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Earlier work this paper cites.
Improving top-N recommendation performance using missing data
Xiangyu Zhao, Zhendong Niu, Kaiyi Wang, Ke Niu, Zhongqiang Liu, et al · 2015
Earlier work this paper cites.
Modeling user exposure in recommendation. In Proceedings of the 25th international conference on World Wide Web . 951–961
Dawen Liang, Laurent Charlin, James McInerney, and David M Blei. 2016 · 2016
Earlier work this paper cites.
Training region-based object detectors with online hard example mining. In Proceedings of the IEEE conference on computer vision and pattern recognition . 761–769
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick. 2016 · 2016
Earlier work this paper cites.
Unsupervised deep embedding for clustering analysis. In International conference on machine learning . PMLR, 478–487
Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016 · 2016
Earlier work this paper cites.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Sampler design for bayesian personalized ranking by leveraging view data
Jingtao Ding, Guanghui Yu, Xiangnan He, Fuli Feng, Yong Li, and Depeng Jin. 2019 · 2019
Earlier work this paper cites.
Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval . 165–174
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
Earlier work this paper cites.
Leveraging post-click feedback for content recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems . 278–286
Hongyi Wen, Longqi Yang, and Deborah Estrin. 2019 · 2019
Cited alongside, same era.
A neural influence diffusion model for social recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval . 235–244
Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang. 2019 · 2019
Cited alongside, same era.
Simplify and robustify negative sampling for implicit collaborative filtering
Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, and Depeng Jin. 2020 · 2020
Cited alongside, same era.
Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Cited alongside, same era.
AutoDenoise: Automatic Data Instance Denoising for Recommendations. In Proceedings of the ACM Web Conference 2023 . 1003–1011
Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, and Wanyu Wang. 2023 · 2023
Later among the works it cites.
On the Theories Behind Hard Negative Sampling for Recommendation. In Proceedings of the ACM Web Conference 2023 . 812–822
Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, and Xiangnan He. 2023 · 2023
Later among the works it cites.
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
Peijie Sun, Le Wu, Kun Zhang, Xiangzhi Chen, and Meng Wang. 2023 · 2023
Later among the works it cites.
Denoised Self-Augmented Learning for Social Recommendation
Tianle Wang, Lianghao Xia, and Chao Huang. 2023c · 2023
Later among the works it cites.
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 932–941
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M2GRL: A multi-task multi-view graph representation learning framework for web-scale recommender systems. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 2349–2358
Menghan Wang, Yujie Lin, Guli Lin, Keping Yang, and Xiao-ming Wu. 2020 · 2020
Cited alongside, same era.
A generalized catoni’s m-estimator under finite α \alpha -th moment assumption with α ∈ \alpha\in (1, 2)
Peng Chen, Xinghu Jin, Xiang Li, and Lihu Xu. 2021 · 2021
Cited alongside, same era.
A loss curvature perspective on training instability in deep learning
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Dahl, Zachary Nado, and Orhan Firat. 2021 · 2021
Cited alongside, same era.
Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data.. In IJCAI . 1579–1585
Yatong Sun, Bin Wang, Zhu Sun, and Xiaochun Yang. 2021 · 2021
Cited alongside, same era.
Sample selection with uncertainty of losses for learning with noisy labels
Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, and Masashi Sugiyama. 2021 · 2021
Cited alongside, same era.
Denoising self-attentive sequential recommendation. In Proceedings of the 16th ACM Conference on Recommender Systems . 92–101
Huiyuan Chen, Yusan Lin, Menghai Pan, Lan Wang, Chin-Chia Michael Yeh, Xiaoting Li, Yan Zheng, Fei Wang, and Hao Yang. 2022 · 2022
Cited alongside, same era.
LCD: Adaptive Label Correction for Denoising Music Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 3903–3907
Quanyu Dai, Yalei Lv, Jieming Zhu, Junjie Ye, Zhenhua Dong, Rui Zhang, Shu-Tao Xia, and Ruiming Tang. 2022 · 2022
Cited alongside, same era.
Self-guided learning to denoise for robust recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1412–1422
Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, and Baihua Zheng. 2022 · 2022
Cited alongside, same era.
Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon M. Jose, Maarten de Rijke, and Zhaochun Ren. 2023 · 2023
Later among the works it cites.
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation
Dezhao Yang, Jianghong Ma, Shanshan Feng, Haijun Zhang, and Zhao Zhang. 2023a · 2023
Later among the works it cites.
Hyperbolic Graph Learning for Social Recommendation
Yonghui Yang, Le Wu, Kun Zhang, Richang Hong, Hailin Zhou, Zhiqiang Zhang, Jun Zhou, and Meng Wang. 2023b · 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.
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, and Yidong Li. 2023b · 2023
Later among the works it cites.
Robust Recommender System: A Survey and Future Directions
Kaike Zhang, Qi Cao, Fei Sun, Yunfan Wu, Shuchang Tao, Huawei Shen, and Xueqi Cheng. 2023a · 2023
Later among the works it cites.
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
Haiyuan Zhao, Lei Zhang, Jun Xu, Guohao Cai, Zhenhua Dong, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
Unified Representation Learning for Discrete Attribute Enhanced Completely Cold-Start Recommendation
Haoyue Bai, Min Hou, Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, and Meng Wang. 2024 · 2024
Closest in time.
Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning
Miaomiao Cai, Min Hou, Lei Chen, Le Wu, Haoyue Bai, Yong Li, and Meng Wang. 2024 · 2024
Closest in time.
Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
Peijie Sun, Yifan Wang, Min Zhang, Chuhan Wu, Yan Fang, Hong Zhu, Yuan Fang, and Meng Wang. 2024 · 2024
Closest in time.
Intersectional Two-sided Fairness in Recommendation. In Proceedings of the ACM on Web Conference 2024 (WWW ’24) . Association for Computing Machinery, New York, NY, USA, 3609–3620
Yifan Wang, Peijie Sun, Weizhi Ma, Min Zhang, Yuan Zhang, Peng Jiang, and Shaoping Ma. 2024 · 2024
Closest in time.
Learning robust recommenders through cross-model agreement. In Proceedings of the ACM Web Conference 2022 . 2015–2025
Yu Wang, Xin Xin, Zaiqiao Meng, Joemon M Jose, Fuli Feng, and Xiangnan He. 2022 · 2025
Closest in time.