Global Context Enhanced Graph Neural Networks for Session-based Recommendation-Reference-Cited by-同舟云学术

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

Published:2020-07-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Wang Ziyang¹,Wei Wei¹,Cong Gao²,Li Xiao-Li³,Mao Xian-Ling⁴,Qiu Minghui⁵

Affiliation:

1. Huazhong University of Science and Technology, Wuhan, China

2. Nanyang Technological University, Singapore, Singapore

3. Institute for Infocomm Research, Singapore, Singapore

4. Beijing Institute of Technology, Beijing, China

5. Alibaba Group, Hangzhou City, China

Funder

National Natural Science Foundation of China

Equipment Pre-Research Fund for The 13th Five-year Plan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3397271.3401142

Reference26 articles.

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2. Balázs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In ICLR. Balázs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In ICLR.

3. Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In RecSys. 306--310. Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In RecSys. 306--310.

4. Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM. 197--206. Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM. 197--206.

5. Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR. Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.

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