Intent Distribution based Bipartite Graph Representation Learning-Reference-Cited by-同舟云学术

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Intent Distribution based Bipartite Graph Representation Learning

Published:2024-07-10 Issue: Volume:35 Page:1649-1658
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Li Haojie¹^ORCID,Wei Wei¹^ORCID,Liu Guanfeng²^ORCID,Liu Jinhuan¹^ORCID,Jiang Feng³^ORCID,Du Junwei¹^ORCID

Affiliation:

1. College of Data Science, Qingdao University of Science and Technology, Qingdao, China

2. School of Computing, Macquarie University, Sydney, Australia

3. School of Information Science & Technology, Qingdao University of Science and Technology, Qingdao, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657739

Reference45 articles.

1. Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).

2. Jiangxia Cao Xixun Lin Shu Guo Luchen Liu Tingwen Liu and BinWang. 2021. Bipartite graph embedding via mutual information maximization. In WSDM. 635--643.

3. Yukuo Cen Jianwei Zhang Xu Zou Chang Zhou Hongxia Yang and Jie Tang. 2020. Controllable multi-interest framework for recommendation. In KDD. 2942-- 2951.

4. Bo Chang, Alexandros Karatzoglou, YuyanWang, Can Xu, Ed H Chi, and Minmin Chen. 2023. Latent user intent modeling for sequential recommenders. In WWW. 427--431.

5. Chih-Ming Chen Chuan-Ju Wang Ming-Feng Tsai and Yi-Hsuan Yang. 2019. Collaborative similarity embedding for recommender systems. In WWW. 2637-- 2643.

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