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Recommender systems (RSs) have become an essential tool for mitigating information overload in a range of real-world applications.
Bpr: Bayesian personalized ranking from implicit feedback
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The efficient imputation method for neighborhood-based collaborative filtering
Y. Ren, G. Li, et al · 2012
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, et al · 2013
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Adam: Adaptive-maximum imputation for neighborhood-based collaborative filtering
Y. Ren, G. Li, et al · 2013
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Optimizing top-n collaborative filtering via dynamic negative item sampling
W. Zhang, T. Chen, et al · 2013
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Recommendations as treatments: Debiasing learning and evaluation
T. Schnabel, A. Swaminathan, et al · 2016
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Adapting to user interest drift for poi recommendation
H. Yin, X. Zhou, et al · 2016
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On sampling strategies for neural network-based collaborative filtering
T. Chen, Y. Sun, et al · 2017
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Causal embeddings for recommendation
S. Bonner and F. Vasile · 2018
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Gain: Missing data imputation using generative adversarial nets
J. Yoon, J. Jordon, et al · 2018
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Rating augmentation with generative adversarial networks towards accurate collaborative filtering
D.-K. Chae, J.-S. Kang, et al · 2019
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Misgan: Learning from incomplete data with generative adversarial networks
S. C.-X. Li, B. Jiang, et al · 2019
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Multimodal review generation for recommender systems
Q. Truong and H. W. Lauw · 2019
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Enhancing collaborative filtering with generative augmentation
Q. Wang, H. Yin, et al · 2019
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Modelling and analysis of temporal preference drifts using a component-based factorised latent approach
F. Zafari, I. Moser, et al · 2019
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Ar-cf: Augmenting virtual users and items in collaborative filtering for addressing cold-start problems
D.-K. Chae, J. Kim, et al · 2020
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Esam: Discriminative domain adaptation with non-displayed items to improve long-tail performance
Z. Chen, R. Xiao, et al · 2020
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Simplify and robustify negative sampling for implicit collaborative filtering
J. Ding, Y. Quan, et al · 2020
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Generate neural template explanations for recommendation
L. Li, Y. Zhang, et al · 2020
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Unbiased recommender learning from missing-not-at-random implicit feedback
Y. Saito, S. Yaginuma, et al · 2020
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Joint item recommendation and attribute inference: An adaptive graph convolutional network approach
L. Wu, Y. Yang, et al · 2020
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Handling missing data with graph representation learning
J. You, X. Ma, et al · 2020
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Connecting user and item perspectives in popularity debiasing for collaborative recommendation
L. Boratto, G. Fenu, et al · 2021
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Automl: A survey of the state-of-the-art
X. He, K. Zhao, et al · 2021
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Mixgcf: An improved training method for graph neural network-based recommender systems
T. Huang, Y. Dong, et al · 2021
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Heterogeneous graph neural network via attribute completion
D. Jin, C. Huo, et al · 2021
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Pattern-enhanced contrastive policy learning network for sequential recommendation
X. Tong, P. Wang, et al · 2021
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Denoising implicit feedback for recommendation
W. Wang, F. Feng, et al · 2021
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Counterfactual data-augmented sequential recommendation
Z. Wang, J. Zhang, et al · 2021
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A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution
C. Wangwatcharakul and S. Wongthanavasu · 2021
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Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system
T. Wei, F. Feng, et al · 2021
Automated data denoising for recommendation
Y. Ge, M. Rahmani, et al · 2023
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Fair attribute completion on graph with missing attributes
D. Guo, Z. Chu, et al · 2023
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Disentangled negative sampling for collaborative filtering
R. Lai, L. Chen, et al · 2023
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Autodenoise: Automatic data instance denoising for recommendations
W. Lin, X. Zhao, et al · 2023
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A self-correcting sequential recommender
Y. Lin, C. Wang, et al · 2023
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Diffusion augmentation for sequential recommendation
Q. Liu, F. Yan, et al · 2023
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Cited alongside, same era.
Enhanced graph learning for collaborative filtering via mutual information maximization
Y. Yang, L. Wu, et al · 2021
Cited alongside, same era.
Dual sparse attention network for session-based recommendation
J. Yuan, Z. Song, et al · 2021
Cited alongside, same era.
Causerec: Counterfactual user sequence synthesis for sequential recommendation
S. Zhang, D. Yao, et al · 2021
Cited alongside, same era.
Causal intervention for leveraging popularity bias in recommendation
Y. Zhang, F. Feng, et al · 2021
Cited alongside, same era.
Disentangling user interest and conformity for recommendation with causal embedding
Y. Zheng, C. Gao, et al · 2021
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Tdtmf: A recommendation model based on user temporal interest drift and latent review topic evolution with regularization factor
H. Ding, Q. Liu, et al · 2022
Cited alongside, same era.
Robust preference-guided denoising for graph based social recommendation
Y. Quan, J. Ding, et al · 2023
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On the theories behind hard negative sampling for recommendation
W. Shi, J. Chen, et al · 2023
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Convnets match vision transformers at scale
S. L. Smith, A. Brock, et al · 2023
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Causal disentangled recommendation against user preference shifts
W. Wang, X. Lin, et al · 2023
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Label denoising through cross-model agreement
Y. Wang, X. Xin, et al · 2023
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Efficient bi-level optimization for recommendation denoising
Z. Wang, M. Gao, et al · 2023
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Llmrec: Large language models with graph augmentation for recommendation
W. Wei, X. Ren, et al · 2023
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Dataset condensation for recommendation
J. Wu, W. Fan, et al · 2023
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Do llms implicitly exhibit user discrimination in recommendation? an empirical study
C. Xu, W. Wang, et al · 2023
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Towards robust neural graph collaborative filtering via structure denoising and embedding perturbation
H. Ye, X. Li, et al · 2023
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Dataset distillation: A comprehensive review
R. Yu, S. Liu, et al · 2023
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Data-centric artificial intelligence: A survey
D. Zha, Z. P. Bhat, et al · 2023
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Robust collaborative filtering to popularity distribution shift
A. Zhang, W. Ma, et al · 2023
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Invariant collaborative filtering to popularity distribution shift
A. Zhang, J. Zheng, et al · 2023
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Denoising and prompt-tuning for multi-behavior recommendation
C. Zhang, R. Chen, et al · 2023
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Sled: Structure learning based denoising for recommendation
S. Zhang, T. Jiang, et al · 2023
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A survey of large language models
W. X. Zhao, K. Zhou, et al · 2023
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Autoac: Towards automated attribute completion for heterogeneous graph neural network
G. Zhu, Z. Zhu, et al · 2023
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Adaptive hardness negative sampling for collaborative filtering
R. Lai, R. Chen, et al · 2024
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