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Data-Driven Diffusion Recommendation in Online Social Networks for the Internet of People.

Authors :
Mumin, Diyawu
Shi, Lei-Lei
Liu, Lu
Panneerselvam, John
Source :
IEEE Transactions on Systems, Man & Cybernetics. Systems. Jan2022, Vol. 52 Issue 1, p166-178. 13p.
Publication Year :
2022

Abstract

Recommendation systems are gaining popularity with the proliferation of the Internet of People (IoP). The popularity and use of online social networks facilitate integrating these social relationships with recommender systems under a single framework of IoP. This article proposes a new approach for item recommendation based on the diffusion method that combines user relationships in social networks with user–item relationships derived from the IoP. Especially, a resource redistribution process is explored in the user–object network that gives mass diffusion a higher recommendation accuracy and heat conduct a greater diversity by considering the social degree of users whilst calculating the user degree in the network. A tuning parameter is introduced to adjust the weight of resources that the objects finally receives from users based on their social relationships. Finally, extensive experiments conducted on the real-world datasets which contain friendship relationships, demonstrate the efficiencies of our proposed method in achieving notable performance improvements in terms of the recommendation accuracy, service diversity, and practical dependability. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21682216
Volume :
52
Issue :
1
Database :
Academic Search Index
Journal :
IEEE Transactions on Systems, Man & Cybernetics. Systems
Publication Type :
Academic Journal
Accession number :
154239882
Full Text :
https://doi.org/10.1109/TSMC.2020.3015355