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Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns.
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Adversarial attack on graph structured data
Dai H, Li H, Tian T, Huang X, Wang L, Zhu J, Song L · 2018
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Neural graph collaborative filtering
Wang X, He X, Wang M, Feng F, Chua T S · 2019
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A neural influence diffusion model for social recommendation
Wu L, Sun P, Fu Y, Hong R, Wang X, Wang M · 2019
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Simplifying graph convolutional networks
Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K · 2019
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Managing popularity bias in recommender systems with personalized re-ranking
Abdollahpouri H, Burke R, Mobasher B · 2019
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Offline evaluation to make decisions about playlistrecommendation algorithms
Gruson A, Chandar P, Charbuillet C, McInerney J, Hansen S, Tardieu D, Carterette B · 2019
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Guo H, Yu J, Liu Q, Tang R, Zhang Y · 2019
Causal intervention for leveraging popularity bias in recommendation
Zhang Y, Feng F, He X, Wei T, Song C, Ling G, Zhang Y · 2021
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Ultragcn: ultra simplification of graph convolutional networks for recommendation
Mao K, Zhu J, Xiao X, Lu B, Wang Z, He X · 2021
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Bilateral filtering graph convolutional network for multi-relational social recommendation in the power-law networks
Zhao M, Deng Q, Wang K, Wu R, Tao J, Fan C, Chen L, Cui P · 2021
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Tail-gnn: Tail-node graph neural networks
Liu Z, Nguyen T K, Fang Y · 2021
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Popularity bias in dynamic recommendation
Zhu Z, He Y, Zhao X, Caverlee J · 2021
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Causer: Causal session-based recommendations for handling popularity bias
Gupta P, Sharma A, Malhotra P, Vig L, Shroff G · 2021
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Lightgcn: Simplifying and powering graph convolution network for recommendation
He X, Deng K, Wang X, Li Y, Zhang Y, Wang M · 2020
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Investigating and mitigating degree-related biases in graph convoltuional networks
Tang X, Yao H, Sun Y, Wang Y, Tang J, Aggarwal C, Mitra P, Wang S · 2020
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Unbiased learning for the causal effect of recommendation
Sato M, Takemori S, Singh J, Ohkuma T · 2020
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Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Chen D, Lin Y, Li W, Li P, Zhou J, Sun X · 2020
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Simple and deep graph convolutional networks
Chen M, Wei Z, Huang Z, Ding B, Li Y · 2020
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Towards deeper graph neural networks
Liu M, Gao H, Ji S · 2020
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Correcting exposure bias for link recommendation
Gupta S, Wang H, Lipton Z, Wang Y · 2021
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Autodebias: Learning to debias for recommendation
Chen J, Dong H, Qiu Y, He X, Xin X, Chen L, Lin G, Yang K · 2021
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A model of two tales: Dual transfer learning framework for improved long-tail item recommendation
Zhang Y, Cheng D Z, Yao T, Yi X, Hong L, Chi E H · 2021
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Causalrec: Causal inference for visual debiasing in visually-aware recommendation
Qiu R, Wang S, Chen Z, Yin H, Huang Z · 2021
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Deconfounded recommendation for alleviating bias amplification
Wang W, Feng F, He X, Wang X, Chua T S · 2021
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A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
Wu L, He X, Wang X, Zhang K, Wang M · 2022
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On the effectiveness of sampled softmax loss for item recommendation
Wu J, Wang X, Gao X, Chen J, Fu H, Qiu T, He X · 2022
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Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation
Zhao Z, Chen J, Zhou S, He X, Cao X, Zhang F, Wu W · 2022
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Neutralizing popularity bias in recommendation models
Xv G, Lin C, Li H, Su J, Ye W, Chen Y · 2022
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Bias and debias in recommender system: A survey and future directions
Chen J, Dong H, Wang X, Feng F, Wang M, He X · 2023
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