An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph-Reference-Cited by-同舟云学术

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

Published:2020-07-06 Issue: Volume: Page:
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Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Jin Jiarui¹,Qin Jiarui¹,Fang Yuchen¹,Du Kounianhua¹,Zhang Weinan¹,Yu Yong¹,Zhang Zheng²,Smola Alexander J.³

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

2. Amazon Web Services, Shanghai, China

3. Amazon Web Services, Palo Alto, CA, USA

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403050

Reference36 articles.

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2. Yuxiao Dong Nitesh V Chawla and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD. Yuxiao Dong Nitesh V Chawla and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD.

3. Wei Feng and Jianyong Wang. 2012. Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In KDD. Wei Feng and Jianyong Wang. 2012. Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In KDD.

4. Tao-yang Fu Wang-Chien Lee and Zhen Lei. 2017. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In CIKM. Tao-yang Fu Wang-Chien Lee and Zhen Lei. 2017. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In CIKM.

5. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD.

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