Multi-Behavior Recommendation with Cascading Graph Convolution Networks-Reference-Cited by-同舟云学术

Multi-Behavior Recommendation with Cascading Graph Convolution Networks

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2023
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Author:

Cheng Zhiyong¹^ORCID,Han Sai¹^ORCID,Liu Fan²^ORCID,Zhu Lei³^ORCID,Gao Zan¹^ORCID,Peng Yuxin⁴^ORCID

Affiliation:

1. Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), China

2. School of Computing, National University of Singapore, Singapore

3. School of Information Science and Engineering, Shandong Normal University, China

4. Wangxuan Institute of Computer Technology, Peking University, China and Peng Cheng Laboratory, China

Funder

the National Key R&D Program of China

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543507.3583439

Reference37 articles.

1. Graph Heterogeneous Multi-Relational Recommendation

2. Lei Chen , Le Wu , Richang Hong , Kun Zhang , and Meng Wang . 2020 . Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In The Thirty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press, 27–34 . Lei Chen, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2020. Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In The Thirty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press, 27–34.

3. Aspect-Aware Latent Factor Model

4. Feature-Level Attentive ICF for Recommendation

5. Improving Implicit Recommender Systems with View Data

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