A Trend of AI Conference Convergence in Similarity: An Empirical Study Through Trans-Temporal Heterogeneous Graph-Reference-Cited by-同舟云学术

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A Trend of AI Conference Convergence in Similarity: An Empirical Study Through Trans-Temporal Heterogeneous Graph

Published:2023-09-01 Issue:9 Volume:35 Page:9642-9655
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Gao Shen¹^ORCID,Zhang Haotong²^ORCID,Chen Xiuying³^ORCID,Tao Chongyang⁴,Zhao Dongyan⁵,Yan Rui⁶^ORCID

Affiliation:

1. Shandong University, Qingdao, China

2. The University of Manchester, Manchester, U.K.

3. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

4. Microsoft, Beijing, China

5. Peking University, Beijing, China

6. Renmin University, Beijing, China

Funder

National Natural Science Foundation of China

Beijing Outstanding Young Scientist Program

Intelligent Social Governance Platform

Major Innovation & Planning Interdisciplinary Platform

Beijing Academy of Artificial Intelligence

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10210449/10049682.pdf?arnumber=10049682

Reference64 articles.

1. OAG

2. Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer

3. Multi-Order Attentive Ranking Model for Sequential Recommendation

4. Style transfer as unsupervised machine translation;zhang,2018

5. An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

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