Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity-Reference-Cited by-同舟云学术

Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity

Published:2023-07-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
language:
Short-container-title:

Author:

Zhang Xiao¹^ORCID,Ye Ziming¹^ORCID,Lu Jianfeng²^ORCID,Zhuang Fuzhen³^ORCID,Zheng Yanwei¹^ORCID,Yu Dongxiao¹^ORCID

Affiliation:

1. Shandong University, Qingdao, China

2. Wuhan University of Science and Technology, Wuhan, China

3. Beihang University, Zhongguancun Laboratory, Beijing, China

Funder

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

the National Key Research and Development Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539618.3591688

Reference36 articles.

1. FG-CF: Friends-aware graph collaborative filtering for POI recommendation

2. Qian Dong , Baisong Liu , Xueyuan Zhang , Jiangcheng Qin , Bingyuan Wang , and Jiangbo Qian . 2022 . Ranking-based Federated POI Recommendation with Geographic Effect. In 2022 International Joint Conference on Neural Networks (IJCNN). 1--8. https://doi.org/10 .1109/IJCNN55064.2022.9892943 10.1109/IJCNN55064.2022.9892943 Qian Dong, Baisong Liu, Xueyuan Zhang, Jiangcheng Qin, Bingyuan Wang, and Jiangbo Qian. 2022. Ranking-based Federated POI Recommendation with Geographic Effect. In 2022 International Joint Conference on Neural Networks (IJCNN). 1--8. https://doi.org/10.1109/IJCNN55064.2022.9892943

3. Alireza Fallah , Aryan Mokhtari , and Asuman Ozdaglar . 2020. Personalized federated learning: A meta-learning approach. arXiv preprint arXiv:2002.07948 ( 2020 ). Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020. Personalized federated learning: A meta-learning approach. arXiv preprint arXiv:2002.07948 (2020).

4. DeepMove

5. PMF