PKR: A personalized knowledge recommendation system for virtual research communities

Hei Chia Wang, Yu Lun Chang

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)


This paper proposes a personalized explicit- and tacit-knowledge recommendation model for a virtual research community. The proposed model aims to recommend both useful journal documents (explicit knowledge) and community members who can discuss the information (tacit knowledge) on-line in real time. A middleware system, the personalized knowledge recommender (PKR) system, that was constructed from this model is presented. The model combines content-based (CB) and collaborative filtering (CF) methods, to make explicit and tacit knowledge recommendations. Unlike other similar systems, this system adapts CB and CF in different ways to provide users with not only "interest- related" documents for reference but also connections to the "related knowledge owners" for further on-line discussion. An e-joumal paper recommendation for a virtual research community is used as an example to evaluate the performance of PKR in terms of mean absolute error, precision, recall and F-measure. PKR creates the specialty of explicit- and tacit-knowledge recommendations.

頁(從 - 到)31-41
期刊Journal of Computer Information Systems
出版狀態Published - 2007 9月

All Science Journal Classification (ASJC) codes

  • 資訊系統
  • 教育
  • 電腦網路與通信


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