Research on optimize an application market’s application recommendation mechanism by quality consideration and intelligent semantic network analysis

• Main Authors: Chang-Yu Jen, 任昶諭
• Other Authors: 柯志坤
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/nb9w4z

碩士 === 國立臺中科技大學 === 資訊管理系碩士班 === 102 === As internet becomes more mature, a wide range of Applications (App) are constantly introduced in the app store platform. When users conduct app search via the search function provided by the platform, the huge app information stored in the platform may cause users not able to find the app meeting their needs among the search results. Although the platform provides search-via-category function, the category in which the app is defined may influence the search result and that certain apps which satisfy users’ needs may not be sorted out. Moreover, when users are making choices, they would not only take the functions into consideration but also the descriptions provided by the platform for reference of choices. Thus, how to design an effective method to be applied in the platform, so that users can obtain apps that meet their needs, is a very worthwhile search topic. Therefore, this study uses app store platform as a research topic to explore the issue of excessive sorted app when users conduct app search in an app store. This study was designed to establish semantic network via data mining technique, and through multi-attribute decision analysis to provide a quality-based intelligent semantic network analysis and recommendation mechanism, hoping to allow the search results further meet users’ needs of apps. Quality-based intelligent semantic network analysis and recommendation mechanism adopts TF-IDF statistical method for unstructured data processing and Top-K method to process the first K-terms among the results via association rule analysis to find identify the implicit association between categories; and establish semantic network through the association of terms. Multi-attribute decision analysis uses the app attribute weight set by users to calculate and ensure the recommended apps can satisfy users’ needs. Furthermore, system evaluation is conducted via Recall, Precise, and F1 indicator, and through UTAUT(Unified Theory of Acceptance &; Use of Technology) and questionnaire survey, the research structure, satisfaction, and users’ willingness to use is further explored. This study was designed to establish semantic network via data mining technique and combines multi-attribute decision analysis to process intelligent recommendation; we believe there is a certain degree of contribution of this study. Future research direction may focus on users’ past usage experience to adjust multi-attribute decision analysis weight matrix. Also, the influence of positive and negative correlation between each attribute can be put into consideration as one of the indicator for multi-attribute decision analysis. Moreover, the use of genetic algorithm, support vector machine and others can be considered as alternative recommendation method to strengthen the result of recommendation.