CrossDomain Recommendation Based on MetaData Using Graph Convolution Networks-Reference-Cited by-同舟云学术

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CrossDomain Recommendation Based on MetaData Using Graph Convolution Networks

Published:2023 Issue: Volume:11 Page:90724-90738
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Khan Rabia¹,Iltaf Naima¹^ORCID,Latif Rabia²^ORCID,Jamail Nor Shahida Mohd²

Affiliation:

1. Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan

2. Artificial Intelligence and Data Analytics Laboratory, College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia

Funder

Prince Sultan University for paying the Article Processing Charges

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10225535.pdf?arnumber=10225535

Reference42 articles.

1. Cross-Domain Recommender Systems

2. Transfer learning to predict missing ratings via heterogeneous user feedbacks;Pan

3. Transfer learning for collaborative filtering via a rating-matrix generative model

4. Recommender systems survey

5. Neural Graph Collaborative Filtering

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. A systematic review of the literature on deep learning approaches for cross-domain recommender systems;Decision Analytics Journal;2024-09

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