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Abstract：
In this paper, we investigate a typical clustering technology, namely, Gaussian mixture model (GMM)-based approach, for user interest prediction in social networks. The establishment of the model follows the following process: collect dataset from 4613 users and more than 16 million messages from Sina Weibo, obtain each user＇s interest eigenvalue sequence and establish GMM model to clustering users. In theory and experiment, this approach is feasible. The GMM-based approach considers the prediction accuracy and consuming time. A series of experiments are conducted to validate the feasibility and efficiency of the proposed solution and whether it can achieve a higher accuracy of prediction compared with other approaches, such as SVM and K-means. Further experiments show that GMM-based approach could produce higher prediction accuracy of 93.9%, thus leveraging computation complexity. © 2015 IEEE.
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Year： 2015
Page： 196-201
Language： English
Cited Count：
SCOPUS Cited Count： 6
ESI Highly Cited Papers on the List： 0 Unfold All
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30 Days PV： 1
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