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

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

Recommender systems are crucial in today’s digital world, by enhancing user engagement experience in digital ecosystems. Internet of things (IoT) have huge potential to generate dynamic and real time data. The data generated through IoT are being utilized to extract dynamic context of the...

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Bibliographic Details
Main Authors: Adeel Ashraf Cheema, Muhammad Shahzad Sarfraz, Muhammad Usman, Qamar Uz Zaman, Usman Habib, Ekkarat Boonchieng
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Cold start problem
context-aware systems (CARS)
cross-domain recommender systems (CDRS)
data sparsity
internet of things (IoT)
knowledge transfer (KT)
Online Access:https://ieeexplore.ieee.org/document/10600697/

Internet

https://ieeexplore.ieee.org/document/10600697/

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