English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113160/144130 (79%)
Visitors : 50760068      Online Users : 676
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 >  Item 140.119/112489


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/112489


    Title: AppReco: Behavior-aware recommendation for iOS mobile applications
    Authors: 方子睿
    黃書韋
    郁方
    Fang, Zih Ruei
    Huang, Shu Wei
    Yu, Fang
    Contributors: 資管系
    Keywords: Bins;Conformal mapping;Data mining;Function evaluation;Indexing (of information);Internet;iOS (operating system);Mobile computing;Mobile telecommunication systems;Recommender systems;Self organizing maps;Statistics;Web services;Websites;Growing hierarchical self-organizing maps;Hier-archical clustering;Internet connection;Latent Dirichlet allocation;Mobile applications;Privacy information;Software applications;Static binary analysis;Application programs
    Date: 2016-08
    Issue Date: 2017-09-01 10:07:05 (UTC+8)
    Abstract: Mobile applications have been widely used in life and become dominant software applications nowadays. However there are lack of systematic recommendation systems that can be leveraged in advance without users` evaluations. We present AppReco, a systematic recommendation system of iOS mobile applications that can evaluate mobile applications without executions. AppReco evaluates apps that have similar interests with static binary analysis, revealing their behaviors according to the embedded functions in the executable. The analysis consists of three stages: (1) unsupervised learning on app descriptions with Latent Dirichlet Allocation for topic discovery and Growing Hierarchical Self-organizing Maps for hierarchical clustering, (2) static binary analysis on executables to discover embedded system calls and (3) ranking common-topic applications from their matched behavior patterns. To find apps that have similar interests, AppReco discovers (unsupervised) topics in official descriptions and clusters apps that have common topics as similar-interest apps. To evaluate apps, AppReco adopts static binary analysis on their executables to count invoked system calls and reveal embedded functions. To recommend apps, AppReco analyzes similar-interest apps with their behaviors of executables, and recommend apps that have less sensitive behaviors such as commercial advertisements, privacy information access, and internet connections, to users. We report our analysis against thousands of iOS apps in the Apple app store including most of the listed top 200 applications in each category.
    Relation: Proceedings - 2016 IEEE International Conference on Web Services, ICWS 2016 , 492-499
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1109/ICWS.2016.70
    DOI: 10.1109/ICWS.2016.70
    Appears in Collections:[資訊管理學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    1-8.pdf349KbAdobe PDF2404View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback