NETWORK-ENABLED KNOWLEDGE DISCOVERY RECOMMENDER SERVICE FOR SOCIAL SEARCH OF CONTENT MANAGEMENT SYSTEM
Wei‐Feng Tung, Ming‐Hsien Yang, Wenkai Liu · Int. J. Electron. Bus. Manag. · 2014
This paper presents a novel content recommender service that uses network-enabled knowledge discovery in database (NKDD), which is developing a social search that is different with traditional keyword search. Specifically, the NKDD in this study combines the content-based term co-occurrence network (TCN) and the user-based collaborative filtering (CF) technologies. At the heart of the research is developing what social search, the integration of content-based and user-based recommendation technologies, calls the NKDD-centered recommender service, which can provide a novel text search. TCN can determine the relevant terms / keywords in order to recommend the other relative online texts / articles. CF decides the recommended texts according to the ratings of the similar users. Because in that list specifically to determine the other online texts. A dataset (OHSUMED) can be searched for online medical articles to demonstrate the recommender service and verifies the performances of precision and recall.