Abstract
Mining mobile users’ interest is very important for numerous of commercial applications such as product recommendation, personalized advertisement, precision marketing, etc. In this paper, we proposed a novel clustering approach for semantic mining from cellular network browsing profiles based on the topic model. We treat each URL as a word and the user’s browsing history as a document, and adopt the Latent Dirichlet Allocation (LDA) model to represent the web browsing interest of mobile users. We further used K-means to cluster the users into several groups according to their topic similarities, and apply a feature ranking approach to explain the sematic meaning of the clustering results. The performance of the proposed approach is verified on a dataset from a telecom operator, which explains users’ interests well in the clusters.
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Acknowledgements
This work was partially supported by the National Key R&D Program of China (Grant No. 2017YFB1001801), the National Natural Science Foundation of China (Grant Nos. 61672278, 61373128, 61321491), the science and technology project from State Grid Corporation of China (Contract No. SGSNXT00YJJS1800031), the Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Sino-German Institutes of Social Computing.
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Yan, F., Ding, Y., Li, W. (2018). Mining Mobile Users’ Interests Through Cellular Network Browsing Profiles. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_71
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DOI: https://doi.org/10.1007/978-3-319-94268-1_71
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