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Implicit feedback, often used to build recommender systems, unavoidably confronts noise due to factors such as misclicks and position bias.
BPR: Bayesian personalized ranking from implicit feedback
Rendle, S.; Freudenthaler, C.; Gantner, Z.; and Schmidt-Thieme, L. 2009 · 2009
Earlier work this paper cites.
Personalized ranking for non-uniformly sampled items
Gantner, Z.; Drumond, L.; Freudenthaler, C.; and Schmidt-Thieme, L. 2012 · 2011
Earlier work this paper cites.
Neural graph collaborative filtering
Wang, X.; He, X.; Wang, M.; Feng, F.; and Chua, T.-S. 2019 · 2019
Earlier work this paper cites.
Simplify and robustify negative sampling for implicit collaborative filtering
Ding, J.; Quan, Y.; Yao, Q.; Li, Y.; and Jin, D. 2020 · 2020
Earlier work this paper cites.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
He, X.; Deng, K.; Wang, X.; Li, Y.; Zhang, Y.; and Wang, M. 2020 · 2020
Earlier work this paper cites.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters
Luo, H.; Zhou, J.; Bao, Z.; Li, S.; Culpepper, J. S.; Ying, H.; Liu, H.; and Xiong, H. 2020 · 2020
Earlier work this paper cites.
Generalized Data Weighting via Class-level Gradient Manipulation
Chen, C.; Zheng, S.; Chen, X.; Dong, E.; Liu, X.; Liu, H.; and Dou, D. 2021 · 2021
Earlier work this paper cites.
Self-supervised graph learning for recommendation
Wu, J.; Wang, X.; Feng, F.; He, X.; Chen, L.; Lian, J.; and Xie, X. 2021 · 2021
Earlier work this paper cites.
Self-guided learning to denoise for robust recommendation
Gao, Y.; Du, Y.; Hu, Y.; Chen, L.; Zhu, X.; Fang, Z.; and Zheng, B. 2022 · 2022
Cited alongside, same era.
Improving graph collaborative filtering with neighborhood-enriched contrastive learning
Lin, Z.; Tian, C.; Hou, Y.; and Zhao, W. X. 2022 · 2022
Cited alongside, same era.
Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering
Tian, C.; Xie, Y.; Li, Y.; Yang, N.; and Zhao, W. X. 2022 · 2022
Cited alongside, same era.
Autodenoise: Automatic data instance denoising for recommendations
Lin, W.; Zhao, X.; Wang, Y.; Zhu, Y.; and Wang, W. 2023 · 2023
Cited alongside, same era.
On the theories behind hard negative sampling for recommendation
Shi, W.; Chen, J.; Feng, F.; Zhang, J.; Wu, J.; Gao, C.; and He, X. 2023 · 2023
Cited alongside, same era.
Efficient bi-level optimization for recommendation denoising
Make Large Language Model a Better Ranker
Chao, W.; Zheng, Z.; Zhu, H.; and Liu, H. 2024 · 2024
Closest in time.
Double Correction Framework for Denoising Recommendation
He, Z.; Wang, Y.; Yang, Y.; Sun, P.; Wu, L.; Bai, H.; Gong, J.; Hong, R.; and Zhang, M. 2024 · 2024
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Representation learning with large language models for recommendation
Ren, X.; Wei, W.; Xia, L.; Su, L.; Cheng, S.; Wang, J.; Yin, D.; and Huang, C. 2024 · 2024
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Llmrec: Large language models with graph augmentation for recommendation
Wei, W.; Ren, X.; Tang, J.; Wang, Q.; Su, L.; Cheng, S.; Wang, J.; Yin, D.; and Huang, C. 2024 · 2024
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A Survey on Large Language Models for Recommendation
Wu, L.; Zheng, Z.; Qiu, Z.; Wang, H.; Gu, H.; Shen, T.; Qin, C.; Zhu, C.; Zhu, H.; Liu, Q.; et al. 2024 · 2024
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Wang, Z.; Gao, M.; Li, W.; Yu, J.; Guo, L.; and Yin, H. 2023 · 2023
Cited alongside, same era.
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
Xi, Y.; Liu, W.; Lin, J.; Cai, X.; Zhu, H.; Zhu, J.; Chen, B.; Tang, R.; Zhang, W.; Zhang, R.; and Yu, Y. 2023 · 2023
Cited alongside, same era.
RLCharge: Imitative Multi-Agent Spatiotemporal Reinforcement Learning for Electric Vehicle Charging Station Recommendation
Zhang, W.; Liu, H.; Xiong, H.; Xu, T.; Wang, F.; Xin, H.; and Wu, H. 2023 · 2023
Cited alongside, same era.
Denoising implicit feedback for recommendation
Wang, W.; Feng, F.; He, X.; Nie, L.; and Chua, T.-S. 2021a
Cited in the paper.
Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue
Wang, W.; Feng, F.; He, X.; Zhang, H.; and Chua, T.-S. 2021b
Cited in the paper.
Implicit feedbacks are not always favorable: Iterative relabeled one-class collaborative filtering against noisy interactions
Wang, Z.; Xu, Q.; Yang, Z.; Cao, X.; and Huang, Q. 2021c
Cited in the paper.
Adapting large language models by integrating collaborative semantics for recommendation
Zheng, B.; Hou, Y.; Lu, H.; Chen, Y.; Zhao, W. X.; Chen, M.; and Wen, J.-R. 2024a · 2024
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Harnessing large language models for text-rich sequential recommendation
Zheng, Z.; Chao, W.; Qiu, Z.; Zhu, H.; and Xiong, H. 2024b · 2024
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Learning robust recommenders through cross-model agreement
Wang, Y.; Xin, X.; Meng, Z.; Jose, J. M.; Feng, F.; and He, X. 2022 · 2025
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