GCNSLIM: Graph convolutional network with sparse linear methods for e-government service recommendation-Reference-Cited by-同舟云学术

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GCNSLIM: Graph convolutional network with sparse linear methods for e-government service recommendation

Published:2024-05 Issue: Volume:292 Page:111593
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Kong Lingyuan,Ding Hao^ORCID,Hu Guangwei^ORCID

Funder

Chinese National Funding of Social Sciences

Publisher

Elsevier BV

Reference44 articles.

1. Knowledge-guided article embedding refinement for session-based news recommendation;Sheu;IEEE Trans. Neural Netw. Learn. Syst.,2021

2. METoNR: A meta explanation triplet oriented news recommendation model;Zhang;Knowl.-Based Syst.,2022

3. Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing;Xia;Int. J. Grid Utility Comput.,2022

4. Music recommender using deep embedding-based features and behavior-based reinforcement learning;Chang;Multimedia Tools Appl.,2021

5. Applying artificial immune systems to collaborative filtering for movie recommendation;Chen;Adv. Eng. Inform.,2015

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Practical Challenges and Methodologies in Next Basket Recommendation (NBR);2024 IEEE International Conference on Electro Information Technology (eIT);2024-05-30

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