TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation-Reference-Cited by-同舟云学术

TAGNN: Target Attentive 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:

Yu Feng¹,Zhu Yanqiao¹,Liu Qiang²,Wu Shu¹,Wang Liang¹,Tan Tieniu¹

Affiliation:

1. Institute of Automation, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China

2. RealAI & Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3397271.3401319

Reference12 articles.

1. 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.

2. Jing Li Pengjie Ren Zhumin Chen Zhaochun Ren Tao Lian and Jun Ma. 2017. Neural Attentive Session-based Recommendation. In CIKM. Jing Li Pengjie Ren Zhumin Chen Zhaochun Ren Tao Lian and Jun Ma. 2017. Neural Attentive Session-based Recommendation. In CIKM.

3. Yujia Li Daniel Tarlow Marc Brockschmidt and Richard Zemel. 2016. Gated Graph Sequence Neural Networks. In ICLR. Yujia Li Daniel Tarlow Marc Brockschmidt and Richard Zemel. 2016. Gated Graph Sequence Neural Networks. In ICLR.

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