Federated learning-driven collaborative recommendation system for multi-modal art analysis and enhanced recommendations
With the rapid development of artificial intelligence technology, recommendation systems have been widely applied in various fi elds. However, in the art fi eld, art similarity search and recommendation systems face unique challenges, namely data privacy and copyright protection issues. To address t...
Published in: | PEERJ COMPUTER SCIENCE |
---|---|
Main Authors: | Gong, Bei; Mahsan, Ida Puteri; Xiao, Junhua |
Format: | Article |
Language: | English |
Published: |
PEERJ INC
2024
|
Subjects: | |
Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-recordWOS:001374782600003 |
Similar Items
-
Federated learning-driven collaborative recommendation system for multi-modal art analysis and enhanced recommendations
by: Gong B.; Mahsan I.P.; Xiao J.
Published: (2024) -
Dynamic temporal reinforcement learning and policy-enhanced LSTM for hotel booking cancellation prediction
by: Xiao, et al.
Published: (2024) -
Personalized Food Recommendations: A Machine Learning Model for Enhanced Dining Choices
by: Al-Hubaishi M.; Ali M.A.M.; Tahir N.M.
Published: (2024) -
Calculus video recommender system
by: Adam N.L.; Aiman Sulaiman M.S.; Soh S.C.
Published: (2019) -
Baristax: The Coffee Selection Recommender Bot
by: Rozaini N.N.; Ariffin N.H.M.; Yusoff M.
Published: (2024)