We use cookies to improve your experience with our site.

Indexed in:

SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.

Submission System
(Author / Reviewer / Editor)
Advanced Search
Volume 30 Issue 5
September  2015
Turn off MathJax
Article Contents
Abstract
References
Related Articles
Yong-Xin Tong, Jieying She, Lei Chen. Towards Better Understanding of App Functions[J]. Journal of Computer Science and Technology, 2015, 30(5): 1130-1140. DOI: 10.1007/s11390-015-1588-0
Citation: Yong-Xin Tong, Jieying She, Lei Chen. Towards Better Understanding of App Functions[J]. Journal of Computer Science and Technology, 2015, 30(5): 1130-1140. DOI: 10.1007/s11390-015-1588-0
shu

Towards Better Understanding of App Functions

  • 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

Funds: 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.
More Information
  • Author Bio:

    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.

  • Corresponding author:

    Yong-Xin Tong E-mail: yxtong@buaa.edu.cn

  • Received Date: November 15, 2014
  • Revised Date: July 21, 2015
  • Published Date: September 04, 2015
    Abstract
  • 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.
    • FullText(HTML)
    • References (24)
  • [1]
    Liu S, Wang S, Zhu F, Zhang J, Krishnan R. HYDRA:Large-scale social identity linkage via heterogeneous behavior modeling. In Proc. ACM SIGMOD, June 2014, pp.51-62.
    [2]
    Tong Y, Cao C C, Chen L. TCS:Efficient topic discovery over crowd-oriented service data. In Proc. the 20th SIGKDD, August 2014, pp.861-870.
    [3]
    Baeza-Yates R, Jiang D, Silvestri F, Harrison B. Predicting the next app that you are going to use. In Proc. the 8th WSDM, February 2015, pp.285-294.
    [4]
    She J, Tong Y, Chen L, Cao C C. Conflict-aware eventparticipant arrangement. In Proc. the 31st ICDE, April 2015, pp.735-746.
    [5]
    She J, Tong Y, Chen L. Utility-aware social eventparticipant planning. In Proc. ACM SIGMOD, May 31-June 4, 2015, pp.1629-1643.
    [6]
    Tong Y, Meng R, She J. On bottleneck-aware arrangement for event-based social networks. In Proc. the 31st ICDE Workshops, April 2015, pp.216-223.
    [7]
    Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3:993-1022.
    [8]
    Griffiths T L, Steyvers M. Finding scientific topics. Proc. the National Academy of Sciences, 2004, 101(Suppl.1):5228-5235.
    [9]
    Jo Y, Oh A H. Aspect and sentiment unification model for online review analysis. In Proc. the 4th WSDM, February 2011, pp.815-824.
    [10]
    Sato I, Nakagawa H. Topic models with power-law using Pitman-Yor process. In Proc. the 16th SIGKDD, July 2010, pp.673-682.
    [11]
    Wang C, Wang J, Xie X, Ma W Y. Mining geographic knowledge using location aware topic model. In Proc. the 4th ACM Workshop on GIR, November 2007, pp.65-70.
    [12]
    Yin Z, Cao L, Han J, Zhai C, Huang T. Geographical topic discovery and comparison. In Proc. the 20th WWW, March 28-April 1, 2011, pp.247-256.
    [13]
    Jiang D, Vosecky J, Leung K W T, Ng W. G-WSTD:A framework for geographic web search topic discovery. In Proc. the 21st CIKM, October 29-November 2, 2012, pp.1143-1152.
    [14]
    Jiang D, Leung K W T, Ng W, Li H. Beyond click graph:Topic modeling for search engine query log analysis. In Proc. the 18th DASFAA, April 2013, pp.209-223.
    [15]
    Sizov S. Geofolk:Latent spatial semantics in Web 2.0 social media. In Proc. the 3rd WSDM, February 2010, pp.281-290.
    [16]
    Eisenstein J, O'Connor B, Smith N A, Xing E P. A latent variable model for geographic lexical variation. In Proc. the EMNLP, October 2010, pp.1277-1287.
    [17]
    Jiang D, Leung K W T, Vosecky J, Ng W. Personalized query suggestion with diversity awareness. In Proc. the 30th ICDE, March 31-April 4, 2014, pp.400-411.
    [18]
    Jiang D, Leung K W T, Ng W. Query intent mining with multiple dimensions of web search data. World Wide Web, 2015.
    [19]
    Hao Q, Cai R, Wang C, Xiao R, Yang J M, Pang Y, Zhang L. Equip tourists with knowledge mined from travelogues. In Proc. the 19th WWW, April 2010, pp.401-410.
    [20]
    Teh Y W. A hierarchical Bayesian language model based on Pitman-Yor processes. In Proc. the 44th ACL, July 2006, pp.985-992.
    [21]
    El-Arini K. Dirichlet Processes:A Gentle Tutorial. 2008. https://www.cs.cmu.edu/~kbe/dptutorial.pdf, Aug. 2015.
    [22]
    Wallach H M. Structured topic models for language[Ph.D. Thesis]. Univ. Cambridge, 2008.
    [23]
    Rosen-ZviM, Griffiths T, Steyvers M, Smyth P. The authortopic model for authors and documents. In Proc. the 20th UAI, July 2004, pp.487-494.
    [24]
    Xia H, Li J, Tang J, Moens M F. Plink-LDA:Using link as prior information in topic modeling. In Proc. the 17th DASFAA, April 2012, pp.213-227.
    • Relative Articles

  • Related Articles

    [1]Dong Liu, Zhi-Lei Ren, Zhong-Tian Long, Guo-Jun Gao, He Jiang. Mining Design Pattern Use Scenarios and Related Design Pattern Pairs: A Case Study on Online Posts[J]. Journal of Computer Science and Technology, 2020, 35(5): 963-978. DOI: 10.1007/s11390-020-0407-4
    [2]Sai-Sai Gong, Wei Hu, Wei-Yi Ge, Yu-Zhong Qu. Modeling Topic-Based Human Expertise for Crowd Entity Resolution[J]. Journal of Computer Science and Technology, 2018, 33(6): 1204-1218. DOI: 10.1007/s11390-018-1882-8
    [3]Yang Li, Wen-Zhuo Song, Bo Yang. Stochastic Variational Inference-Based Parallel and Online Supervised Topic Model for Large-Scale Text Processing[J]. Journal of Computer Science and Technology, 2018, 33(5): 1007-1022. DOI: 10.1007/s11390-018-1871-y
    [4]Tie-Ke He, Hao Lian, Ze-Min Qin, Zhen-Yu Chen, Bin Luo. PTM: A Topic Model for the Inferring of the Penalty[J]. Journal of Computer Science and Technology, 2018, 33(4): 756-767. DOI: 10.1007/s11390-018-1854-z
    [5]Peng-Peng Zhao, Hai-Feng Zhu, Yanchi Liu, Zi-Ting Zhou, Zhi-Xu Li, Jia-Jie Xu, Lei Zhao, Victor S. Sheng. A Generative Model Approach for Geo-Social Group Recommendation[J]. Journal of Computer Science and Technology, 2018, 33(4): 727-738. DOI: 10.1007/s11390-018-1852-1
    [6]Fei-Fei Kou, Jun-Ping Du, Cong-Xian Yang, Yan-Song Shi, Wan-Qiu Cui Mei-Yu Liang, Yue Geng. Hashtag Recommendation Based on Multi-Features of Microblogs[J]. Journal of Computer Science and Technology, 2018, 33(4): 711-726. DOI: 10.1007/s11390-018-1851-2
    [7]Xi-Ming Li, Ji-Hong Ouyang. Tuning the Learning Rate for Stochastic Variational Inference[J]. Journal of Computer Science and Technology, 2016, 31(2): 428-436. DOI: 10.1007/s11390-016-1636-4
    [8]Xin Xin, Chin-Yew Lin, Xiao-Chi Wei, He-Yan Huang. When Factorization Meets Heterogeneous Latent Topics: An Interpretable Cross-Site Recommendation Framework[J]. Journal of Computer Science and Technology, 2015, 30(4): 917-932. DOI: 10.1007/s11390-015-1570-x
    [9]Peng Li, Bin Wang, Wei Jin. ImprovingWeb Document Clustering through Employing User-Related Tag Expansion Techniques[J]. Journal of Computer Science and Technology, 2012, 27(3): 554-566. DOI: 10.1007/s11390-012-1243-y
    [10]Xiao-Jun Wan, Yu-Xin Peng. A New Retrieval Model Based on TextTiling for Document Similarity Search[J]. Journal of Computer Science and Technology, 2005, 20(4): 552-558.
    • Supplements (0)

Catalog

    Get Citation
    PDF
    XML
    Article views (42) PDF downloads (1213) Cited by()
    Related

    Copyright ©2025 JCST, All Rights Reserved     京ICP备05002829号-1

    Institute of Computing Technology, Chinese Academy of Sciences
    P.O. Box 2704, Beijing 100190, P.R. China

    Tel.: 86-10-62610746 E-mail: jcst@ict.ac.cn

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return