A Study on the Optimization of Application Recommendation Mechanism Using Skyline

• Main Authors: Chiao-Min Chang, 張巧旻
• Other Authors: Chih-Kun Ke
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/k95sj2

碩士 === 國立臺中科技大學 === 資訊管理系碩士班 === 103 === The percentage of Internet users around the globe has jumped from 0.6% in 1995 to 39% in 2014, suggesting that people have become accustomed to the convenience brought forth by the Internet, which also promoted the development of mobile phone, as exemplified by the growth of mobile device users around the world from 1% in 1995 to 73% in 2014, among which 40% are smartphone users. Smart mobile devices acquire added features through downloading applications (APP) from application market, spawning a great diversity of applications on nowadays application market, which in turn makes it hard for users to search for the ones that satisfy their needs among a staggeringly large amount of applications. Notwithstanding current Internet search technology features keyword search, category search, popularity search, semantic network. But when using keyword search, users have to clearly understand their needs before finding the corresponding applications swiftly. Yet category search and popularity search merely rank according to the data of a single application. This is not enough for users since they may take information such as price, file size, evaluation, and information description into consideration. The semantic network, on the other hand, may recommend less relevant applications to users due to the deletion of a part of relevant information caused by threshold value settings Therefore, designing an effective method which can be incorporated into the search system of application market and recommend applications that satisfy the users’ needs has become an issue worth studying. This study discusses how searching for applications satisfying the users’ needs among a large amount of applications turns out to be a herculean task. This study uses Skyline to establish Skyline Semantic Network, with a view to remedying the deficiencies of the overall semantic network. In addition, through multiple-criteria decision analysis, this study provides Skyline to optimize application recommendation mechanisms so as to make the recommended applications more satisfying to users. Moreover, this study adopts TF-IDF to capture words from structured applications, and, through association rules, establishes an overall semantic network. Afterwards, through Skyline, this study eliminates inferior applications within the application clusters containing keywords typed in by users, establishes Skyline semantic network through Skyline search clusters, reinforces the overall semantic network through Skyline semantic network, and combines the optimized applications recommended by ELECTRE in multi-attribute decision making analysis. Precision rate, recall rate, and F1-Measure are also used to evaluate related past studies. This study adopts Skyline in optimizing the recommendation mechanism of application market, and is generally applicable to recommendation mechanisms. On top of that, methods such as principal component analysis and information gain can be adopted in the attribute selection of application in future studies. The establishment of semantic network can be completed through formal concept analysis. During the recommendation process of semantic network, methods such as greedy method, dynamic programming and circular path analysis can be adopted to recommend different applications.