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    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/63215
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/63215


    Title: 智慧型手機的使用者行為模式分析
    Behavior Analysis Based on Smart-phone User Logs
    Authors: 許志毓
    Hsu, Chih Yu
    Contributors: 廖文宏
    Liao, Wen Hung
    許志毓
    Hsu, Chih Yu
    Keywords: 行為模式
    使用習慣
    特徵相似度
    序列比對
    behavior pattern
    app usage
    feature similarity
    sequence matching
    Date: 2013
    Issue Date: 2014-01-02 14:06:56 (UTC+8)
    Abstract: 通訊技術的演化與智慧型手機的普及,改變了人際溝通的方式與手機的應用情境,在此變動快速的行動運算時代,欲研究探討使用者的行為模式,必須建立一個包含硬體、軟體與使用者社群的實驗平台,以量化的數據補強質性的觀察,準此,本論文將以現有之平台為基礎,強化其功能與易用性,方便其他研究者觀察資料的概況,並擷取符合某些條件之資料,此外,我們採用3-gram之應用程式序列,作為行為模式(behavior pattern)之特徵定義,配合不同的應用程式被使用之頻率,在相似度比較上進行不同比重的加權,根據實驗結果,可大致對使用者進行初步的分類,亦可利用此指標,針對已分類過的使用者更進一步探討之間的歧異程度。
    The rapid evolution of information technology and prevalence of smart-phones have changed the way people communicate. To effectively observe and investigate user behavior in this new era of mobile computing, an experimental platform that consists of hardware devices, software applications and user groups is essential. In this thesis, we enhance and extend the functions of a user log collection and analysis system to facilitate quick overview of the recorded data and allow flexible query/extraction of desired data segments for further processing. In addition, we employ 3-gram app log sequence as the main feature to characterize user behavior. A similarity measure that takes into account the relative app usage frequency has been defined to compare and classify users and their usage patterns. Experimental results indicate that this measure can effectively distinguish users of different traits given enough time period of observation.
    Reference: [1] Po-Ming Chen, Hui-Yin Wu, Chih-Yu Hsu, Wen-Hung Liao, Tsai-Yen Li Logging and analyzing long-term mobile user behaviors, International Journal of Electronic Commerce Studies, 2012.
    [2] N. Eagle, and A. Pentland, Reality mining: sensing complex social systems. Personal Ubiquitous Computing, 10(4), 255-268, 2006.
    [3] I. Pramudiono, T. Shintani, K. Takahashi, and M. Kitsuregawa: User Behavior Analysis of Location Aware Search Engine. Mobile Data Management, 139-145, 2002.
    [4] R. A. Baeza-Yates. Applications of web query mining. ECIR, 7-22, 2005.
    [5] N. Ducheneaut, N. Yee, E. Nickell, and R. J. Moore, The life and death of online gaming communities: a look at guilds in world of warcraft. Proceedings of the SIGCHI conference on Human factors in computing systems, 2007.
    [6] J. Kort, and H. de Poot, Usage Analysis: Combining Logging and Qualitative Methods. Proceedings of the 2005 CHI Conference on Human Factors in Computing Systems (Extended Abstracts), Vienna: ACM Press, 2121-2122, 2005.
    [7] D. M. Hilbert, and D. F. Redmiles, Extracting Usability Information from User Interface Events. ACM Computing Surveys 2000, Vol. 32, No.4, 384-421, 2000.
    [8] Naaman, Mor, Rahul Nair, and Vlad Kaplun. "Photos on the go: a mobile application case study." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2008.
    [9] Lee, Alison. "Exploiting context for mobile user experience." Position Papers for the 1st Workshop on Semantic Models for Adaptive Interactive Systems (SEMAIS 2010). 2010.
    [10] Baeza-Yates, Roberto. "Web usage mining in search engines." Web mining: applications and techniques (2004): 307-321.
    Description: 碩士
    國立政治大學
    資訊科學學系
    100753028
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100753028
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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