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Abstract：
Efficient interest prediction for social networks is critical for both users and service providers for behavior analysis and a series of extension services. However, most existing approaches are inefficient, incomplete or isolated. In this paper, we propose combination of Gaussian and Markov approaches (namely, GAM) as typical soft computing technology for interest prediction of social intelligent multimedia systems. GAM model considers ＇the number of posted messages＇ as the only parameter, and defines selection logic to implement either Gaussian or Markov based approaches. Our proposed solution takes the advantage of Gaussian model in prediction accuracy and computation complexity, and advantage of Markov model in high availability. Further experiments illustrate that our solution achieves higher prediction accuracy of 94.3% (without considering the influence of swing users), with the best result achieved ever. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
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Multimedia Tools and Applications
ISSN： 1380-7501
Year： 2019
Issue： 23
Volume： 78
Page： 32755-32774
2 . 3 1 3
JCR@2019
3 . 0 0 0
JCR@2023
ESI HC Threshold：162
JCR Journal Grade：2
CAS Journal Grade：3
Cited Count：
SCOPUS Cited Count： 6
ESI Highly Cited Papers on the List： 0 Unfold All
WanFang Cited Count：
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30 Days PV： 1
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