| Summary: | 碩士 === 國立臺中科技大學 === 資訊管理系碩士班 === 104 === As the internet becomes more sophisticated, the demand for Internet resources on smart mobile devices increases rapidly. More and more people turn to their smart mobile devices to obtain information and services in daily life. Since there are an increasing number of applications in the application markets, it has become quite difficult for users to search and identify an application that meets their requirements.
Beginning in 2014, researchers sought to address this growing problem and proposed the use of smart semantic network analysis to optimize the application recommendation mechanism in application markets. In 2015, researchers suggested the use of Skyline as a potential solution. However, these researchers adopted a single multi-criteria decision-making analysis method for the recommendation mechanism. This method can result in inaccurate recommendations relative to other multi-criteria decision-making analysis methods and therefor may produce inappropriate or inaccurate recommendations.
Since the single multi-criteria decision-making analysis method may produce sub-optimal results, this study used several kinds of multi-criteria decision-making analysis methods for analysis and comparison: TOPSIS, VIKOR, ELECTRE, AHP, PROMETHEE, and SAW. The study used precision, recall, and FI-measure to evaluate the various methods and verify whether the single multi-criteria decision-making analysis method adopted by previous researchers is better than other multi-criteria decision-making analysis methods and to identify which of the multi-criteria decision-making analysis methods is best for the recommendation mechanism of application markets or real-time recommendation systems.
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