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題名 AppReco: Behavior-aware recommendation for iOS mobile applications
作者 方子睿
黃書韋
郁方
Fang, Zih Ruei
Huang, Shu Wei
Yu, Fang
貢獻者 資管系
關鍵詞 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
日期 2016-08
上傳時間 1-Sep-2017 10:07:05 (UTC+8)
摘要 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.
關聯 Proceedings - 2016 IEEE International Conference on Web Services, ICWS 2016 , 492-499
資料類型 conference
DOI http://dx.doi.org/10.1109/ICWS.2016.70
dc.contributor 資管系
dc.creator (作者) 方子睿zh_TW
dc.creator (作者) 黃書韋zh_TW
dc.creator (作者) 郁方zh_TW
dc.creator (作者) Fang, Zih Rueien_US
dc.creator (作者) Huang, Shu Weien_US
dc.creator (作者) Yu, Fangen_US
dc.date (日期) 2016-08
dc.date.accessioned 1-Sep-2017 10:07:05 (UTC+8)-
dc.date.available 1-Sep-2017 10:07:05 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2017 10:07:05 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/112489-
dc.description.abstract (摘要) 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.
dc.format.extent 357757 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Proceedings - 2016 IEEE International Conference on Web Services, ICWS 2016 , 492-499en_US
dc.subject (關鍵詞) 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
dc.title (題名) AppReco: Behavior-aware recommendation for iOS mobile applicationsen_US
dc.type (資料類型) conference
dc.identifier.doi (DOI) 10.1109/ICWS.2016.70
dc.doi.uri (DOI) http://dx.doi.org/10.1109/ICWS.2016.70