Study on Iterative Articles Re-Searching with Personal Explicit Preferences
| Autor: | Lin, Yuan-Shu, 林圓淑 |
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| Rok vydání: | 2013 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 101 In recent years, as the internet grows and Web2.0 evolves, the network is flooded with huge amount of data, so to help users quickly dig out useful information has become an important issue. By creating tags, the personalized recommendation systems allow users to directly participate in the process of information dissemination and sharing, which effectively eliminates the phenomenon of information overloading. However, most recommendation systemsuse indirect analysis of user's interests, habits or other community association to strengthen the recommendation effects, but such an implicit learning process is prone to provideinappropriatearticles and will reduce the correctness and fitness of the suggested articles. In this study, first, we build an iterative article Re-Searching system with particular emphasis on the interactive process between the users and the system; it allows users to explicitly filter out important user preferencespersonally by an iterative and accumulative approach.Second, we calculate the article grade by applying the weighting and scoring rules, this may bring out the articles hidden in the middle or the back of article lists that are easily overlooked. Finally, users could judge the availability of the searching results by evaluating the convergence degree. To sum up, this study can be expected to provide searching article lists that are more accurate and more effective to becompliantwith user satisfaction. Keyword: citeulike, iterative, personal, explicit, preference |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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