TY - GEN
T1 - GLDA-FP
T2 - 7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018
AU - Xiao, Yunpeng
AU - Liu, Liangyun
AU - Xu, Ming
AU - Wang, Haohan
AU - Liu, Yanbing
N1 - Funding Information:
Acknowledgements. This paper is partially supported by the National 973 Key Basic Research Program of China (Grant No.2013CB329606), the National Natural Science Foundation of China (Grant No.61772098), Chongqing Science and Technology Commission Project (Grant No.cstc 2017jcyjAX0099) and Chongqing key research and development project (Grant No.cstc2017 zdcy-zdyf0299, cstc2017zdcy-zdyf0436).
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - In social networks, information propagation is affected by diversity factors. In this work, we study the formation of forward behavior, map into multidimensional driving mechanisms and apply the behavioral and structural features to forward prediction. Firstly, by considering the effect of behavioral interest, user activity and network influence, we propose three driving mechanisms: interest-driven, habit-driven and structure-driven. Secondly, by taking advantage of the Latent Dirichlet allocation (LDA) model in dealing with problems of polysemy and synonymy, the traditional text modeling method is improved by Gaussian distribution and applied to user interest, activity and influence modeling. In this way, the user topic distribution for each dimension can be obtained regardless of whether the word is discrete or continuous. Moreover, the model can be extended using the pre-discretizing method which can help LDA detect the topic evolution automatically. By introducing time information, we can dynamically monitor user activity and mine the hidden behavioral habit. Finally, a novel model, Gaussian LDA, for forward prediction is proposed. The experimental results indicate that the model not only mine user latent interest, but also improve forward prediction performance effectively.
AB - In social networks, information propagation is affected by diversity factors. In this work, we study the formation of forward behavior, map into multidimensional driving mechanisms and apply the behavioral and structural features to forward prediction. Firstly, by considering the effect of behavioral interest, user activity and network influence, we propose three driving mechanisms: interest-driven, habit-driven and structure-driven. Secondly, by taking advantage of the Latent Dirichlet allocation (LDA) model in dealing with problems of polysemy and synonymy, the traditional text modeling method is improved by Gaussian distribution and applied to user interest, activity and influence modeling. In this way, the user topic distribution for each dimension can be obtained regardless of whether the word is discrete or continuous. Moreover, the model can be extended using the pre-discretizing method which can help LDA detect the topic evolution automatically. By introducing time information, we can dynamically monitor user activity and mine the hidden behavioral habit. Finally, a novel model, Gaussian LDA, for forward prediction is proposed. The experimental results indicate that the model not only mine user latent interest, but also improve forward prediction performance effectively.
KW - Forward prediction
KW - LDA
KW - Multidimensional driving mechanisms
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UR - http://www.scopus.com/inward/citedby.url?scp=85049367736&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-94301-5_10
DO - 10.1007/978-3-319-94301-5_10
M3 - Conference contribution
AN - SCOPUS:85049367736
SN - 9783319943008
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 124
EP - 139
BT - Big Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings
A2 - Khan, Latifur
A2 - Zhang, Liang-Jie
A2 - Lee, Kisung
A2 - Chin, Francis Y.
A2 - Chen, C. L.
PB - Springer
Y2 - 25 June 2018 through 30 June 2018
ER -