PKR: A personalized knowledge recommendation system for virtual research communities

Hei Chia Wang, Yu Lun Chang

Research output: Contribution to journalArticlepeer-review

13 Citations (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.

Original languageEnglish
Pages (from-to)31-41
Number of pages11
JournalJournal of Computer Information Systems
Issue number1
Publication statusPublished - 2007 Sept

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Education
  • Computer Networks and Communications


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