KT-CDULF: Knowledge Transfer in Context-Aware Cross-Domain Recommender Systems via Latent User Profiling-Reference-Cited by-同舟云学术

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KT-CDULF: Knowledge Transfer in Context-Aware Cross-Domain Recommender Systems via Latent User Profiling

Published:2024 Issue: Volume:12 Page:102111-102125
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ashraf Cheema Adeel¹^ORCID,Shahzad Sarfraz Muhammad¹^ORCID,Usman Muhammad¹^ORCID,Uz Zaman Qamar¹^ORCID,Habib Usman²^ORCID,Boonchieng Ekkarat³^ORCID

Affiliation:

1. Department of Computer Science, FAST National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot, Pakistan

2. Department of Software Engineering, FAST National University of Computer and Emerging Sciences, Islamabad Campus, Islamabad, Pakistan

3. Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand

Funder

Fundamental Fund 2024, Chiang Mai University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10600697.pdf?arnumber=10600697

Reference51 articles.

1. Data Scarcity in Recommendation Systems: A Survey

2. Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems

3. Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

4. Cold start aware hybrid recommender system approach for E-commerce users

5. Wearable IoT intelligent recommender framework for a smarter healthcare approach

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