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In this paper, we propose a novel model named DemiNet (short for DEpendency-Aware Multi-Interest Network) to address the above two issues.
Deep session interest network for click-through rate prediction
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Dynamic routing between capsules
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Deep interest network for click-through rate prediction
Zhou, G.; Zhu, X.; Song, C.; Fan, Y.; Zhu, H.; Ma, X.; Yan, Y.; Jin, J.; Li, H.; and Gai, K. 2018 · 2018
Cited alongside, same era.
Multi-interest network with dynamic routing for recommendation at Tmall
Li, C.; Liu, Z.; Wu, M.; Xu, Y.; Zhao, H.; Huang, P.; Kang, G.; Chen, Q.; Li, W.; and Lee, D. L. 2019 · 2019
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Session-based recommendation with graph neural networks
Wu, S.; Tang, Y.; Zhu, Y.; Wang, L.; Xie, X.; and Tan, T. 2019 · 2019
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Graph Contextualized Self-Attention Network for Session-based Recommendation
Xu, C.; Zhao, P.; Liu, Y.; Sheng, V. S.; Xu, J.; Zhuang, F.; Fang, J.; and Zhou, X. 2019 · 2019
Cited alongside, same era.
Deep interest evolution network for click-through rate prediction
Zhou, G.; Mou, N.; Fan, Y.; Pi, Q.; Bian, W.; Zhou, C.; Zhu, X.; and Gai, K. 2019 · 2019
Cited alongside, same era.
Controllable multi-interest framework for recommendation
Disentangled self-supervision in sequential recommenders
Ma, J.; Zhou, C.; Yang, H.; Cui, P.; Wang, X.; and Zhu, W. 2020 · 2020
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Am-gcn: Adaptive multi-channel graph convolutional networks
Wang, X.; Zhu, M.; Bo, D.; Cui, P.; Shi, C.; and Pei, J. 2020 · 2020
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Deep Multi-Interest Network for Click-through Rate Prediction
Xiao, Z.; Yang, L.; Jiang, W.; Wei, Y.; Hu, Y.; and Wang, H. 2020 · 2020
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Sequential Recommendation with Graph Neural Networks
Chang, J.; Gao, C.; Zheng, Y.; Hui, Y.; Niu, Y.; Song, Y.; Jin, D.; and Li, Y. 2021 · 2021
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Extracting attentive social temporal excitation for sequential recommendation
Li, Y.; Ding, Y.; Chen, B.; Xin, X.; Wang, Y.; Shi, Y.; Tang, R.; and Wang, D. 2021 · 2021
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Cen, Y.; Zhang, J.; Zou, X.; Zhou, C.; Yang, H.; and Tang, J. 2020 · 2020
Cited alongside, same era.
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.; and Sun, X. 2020 · 2020
Cited alongside, same era.
Handling information loss of graph neural networks for session-based recommendation
Chen, T.; and Wong, R. C.-W. 2020 · 2020
Cited alongside, same era.
Lightgcn: Simplifying and powering graph convolution network for recommendation
He, X.; Deng, K.; Wang, X.; Li, Y.; Zhang, Y.; and Wang, M. 2020 · 2020
Cited alongside, same era.
An efficient neighborhood-based interaction model for recommendation on heterogeneous graph
Jin, J.; Qin, J.; Fang, Y.; Du, K.; Zhang, W.; Yu, Y.; Zhang, Z.; and Smola, A. J. 2020 · 2020
Cited alongside, same era.
AIRec: Attentive intersection model for tag-aware recommendation
Chen, B.; Ding, Y.; Xin, X.; Li, Y.; Wang, Y.; and Wang, D. 2021a
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Graph Heterogeneous Multi-Relational Recommendation
Chen, C.; Ma, W.; Zhang, M.; Wang, Z.; He, X.; Wang, C.; Liu, Y.; and Ma, S. 2021b
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Shi, F.; Jin, A. Y.; and Zhu, S.-C. 2021 · 2021
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ICMT: Item Cluster-Wise Multi-Objective Training for Long-Tail Recommendation
Wang, Y.; Xin, X.; Ding, Y.; and Wang, D. 2021 · 2021
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Self-supervised graph learning for recommendation
Wu, J.; Wang, X.; Feng, F.; He, X.; Chen, L.; Lian, J.; and Xie, X. 2021 · 2021
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Task aligned meta-learning based augmented graph for cold-start recommendation
Shi, Y.; Ding, Y.; Chen, B.; Huang, Y.; Wang, Y.; Tang, R.; and Wang, D. 2022 · 2022
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Heterogeneous graph attention network
Wang, X.; Ji, H.; Shi, C.; Wang, B.; Ye, Y.; Cui, P.; and Yu, P. S. 2019 · 2032
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