GraphSAGE-based POI Recommendation via Continuous-Time Modeling-Reference-Cited by-同舟云学术

GraphSAGE-based POI Recommendation via Continuous-Time Modeling

Published:2024-05-13 Issue: Volume:31 Page:585-588
ISSN:
Container-title:Companion Proceedings of the ACM Web Conference 2024
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Author:

Liu Yuwen¹^ORCID,Qi Lianyong¹^ORCID,Liu Weiming²^ORCID,Xu Xiaolong³^ORCID,Zhang Xuyun⁴^ORCID,Dou Wanchun⁵^ORCID

Affiliation:

1. China University of Petroleum (East China), Qingdao, China

2. Zhejiang University, Hangzhou, China

3. Nanjing University of Information Science and Technology, Nanjing, China

4. Macquarie University, Sydney, Australia

5. Nanjing University, Nanjing, China

Funder

Natural Science Foundation of Shandong Province

National Natural Science Foundation of China under Grant

ARC DECRA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589335.3651515

Reference16 articles.

1. Ramesh Baral Dingding Wang Tao Li and Shu-Ching Chen. 2016. Geotecs: exploiting geographical temporal categorical and social aspects for personalized poi recommendation. In IRI. 94--101.

2. Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018. Neural Ordinary Differential Equations. In NIPS, Vol. 31.

3. Tianwen Chen and Raymond Chi-Wing Wong. 2020. Handling information loss of graph neural networks for session-based recommendation. In SIGKDD. 1172--1180.

4. Jiayan Guo Peiyan Zhang Chaozhuo Li Xing Xie Yan Zhang and Sunghun Kim. 2022. Evolutionary preference learning via graph nested gru ode for session-based recommendation. In CIKM. 624--634.

5. Priyanka Gupta, Diksha Garg, Pankaj Malhotra, Lovekesh Vig, and Gautam Shroff. 2019. NISER: Normalized item and session representations to handle popularity bias. arXiv preprint arXiv:1909.04276 (2019).

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