›› 2015, Vol. 30 ›› Issue (5): 1130-1140.doi: 10.1007/s11390-015-1588-0

Special Issue: Data Management and Data Mining

• Special Section on Social Media Processing • Previous Articles     Next Articles

Towards Better Understanding of App Functions

Yong-Xin Tong1,2*(童咏昕), Member, CCF, ACM, IEEE, Jieying She3(佘洁莹), Student Member, IEEE,Lei Chen3(陈雷), Member, ACM, IEEE   

  1. 1 State Key Laboratory of Software Development Environment, School of Computer Science and Engineering Beihang University, Beijing 100191, China;
    2 International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China;
    3 Department of Computer Science and Engineering, The Hong Kong University of Science and Technology Hong Kong, China
  • Received:2014-11-16 Revised:2015-07-22 Online:2015-09-05 Published:2015-09-05
  • Contact: Yong-Xin Tong E-mail:yxtong@buaa.edu.cn
  • About author:Yong-Xin Tong received his Ph.D. degree in computer science and engineering from the Hong Kong University of Science and Technology (HKUST), Hong Kong, in 2014. He is currently an associate professor in the School of Computer Science and Engineering, Beihang University, Beijing. Before that, he served as a research assistant professor and a postdoctoral fellow at HKUST. He is a member of CCF, ACM, and IEEE. His research interests include crowdsourcing, uncertain data mining and management, and social network analysis.
  • Supported by:

    This work is supported in part by the Hong Kong RGC Project under Grant No. N HKUST637/13, the National Basic Research 973 Program of China under Grant No. 2014CB340303, the National Natural Science Foundation of China under Grant Nos. 61328202 and 61502021, Microsoft Research Asia Gift Grant, Google Faculty Award 2013, and Microsoft Research Asia Fellowship 2012.

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Apps are attracting more and more attention from both mobile and web platforms. Due to the self-organized nature of the current app marketplaces, the descriptions of apps are not formally written and contain a lot of noisy words and sentences. Thus, for most of the apps, the functions of them are not well documented and thus cannot be captured by app search engines easily. In this paper, we study the problem of inferring the real functions of an app by identifying the most informative words in its description. In order to utilize and integrate the diverse information of the app corpus in a proper way, we propose a probabilistic topic model to discover the latent data structure of the app corpus. The outputs of the topic model are further used to identify the function of an app and its most informative words. We verify the effectiveness of the proposed methods through extensive experiments on two real app datasets crawled from Google Play and Windows Phone Store, respectively.

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