Behavior Pattern Mining-based Multi-Behavior Recommendation-Reference-Cited by-同舟云学术

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Behavior Pattern Mining-based Multi-Behavior Recommendation

Published:2024-07-10 Issue: Volume:35 Page:2291-2295
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,Cheng Zhiyong²^ORCID,Yu Xu³^ORCID,Liu Jinhuan¹^ORCID,Liu Guanfeng⁴^ORCID,Du Junwei¹^ORCID

Affiliation:

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

2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China

3. College of Computer Science and Technology, China University of Petroleum, Qingdao, China

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

Funder

National Natural Science Foundation of China

the NaturalScience Foundation of Shandong Province

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657973

Reference25 articles.

1. Xuheng Cai Chao Huang Lianghao Xia and Xubin Ren. 2023. LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. In ICLR.

2. Chong Chen Weizhi Ma Min Zhang Zhaowei Wang Xiuqiang He Chenyang Wang Yiqun Liu and Shaoping Ma. 2021. Graph heterogeneous multi-relational recommendation. In AAAI. 3958--3966.

3. Chong Chen Min Zhang Yongfeng Zhang Weizhi Ma Yiqun Liu and Shaoping Ma. 2020. Efficient heterogeneous collaborative filtering without negative sampling for recommendation. In AAAI. 19--26.

4. Zhiyong Cheng Sai Han Fan Liu Lei Zhu Zan Gao and Yuxin Peng. 2023. Multi-Behavior Recommendation with Cascading Graph Convolution Networks. In WWW. 1181--1189.

5. Shuyun Gu Xiao Wang Chuan Shi and Ding Xiao. 2022. Self-supervised graph neural networks for multi-behavior recommendation. In IJCAI.

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