Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Siteseer: personalized navigation for the Web
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
ACM Computing Surveys (CSUR)
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Information Retrieval
Modern Information Retrieval
Context-Aware, Proactive Delivery of Task-Specific Information: The KnowMore Project
Information Systems Frontiers
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Incremental click-stream tree model: Learning from new users for web page prediction
Distributed and Parallel Databases
Discovery of knowledge flow in science
Communications of the ACM - Two decades of the language-action perspective
Knowledge flow network planning and simulation
Decision Support Systems
Mining changes in customer buying behavior for collaborative recommendations
Expert Systems with Applications: An International Journal
Task-based K-Support system: disseminating and sharing task-relevant knowledge
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Knowledge is a critical property that organizations use to gain and maintain competitive advantages. In the constantly changing business environment, organizations have to exploit effective and efficient approaches to help knowledge workers find task-relevant knowledge, as well as to preserve, share and reuse such knowledge. Hence, an important issue is how to discover knowledge flow (KF) from the historical work records of knowledge workers in order to understand their task-needs and the ways they reference documents, and actively provide adaptive knowledge support. This work proposes a KFbased document recommendation method that integrates KF mining and collaborative filtering recommendation mechanisms to recommend codified knowledge. The approach consists of two phases: the KF mining phase and the recommendation phase. The KF mining phase can identify each worker's knowledge flow by considering the referencing time and citation relations of knowledge resources. Then, based on the discovered KF, the recommendation phase applies sequential rule mining and the CF method to recommend relevant documents to the target worker. Experiments are conducted to evaluate the performance of the proposed method and compare it with the traditional CF method using data collected from a research institute laboratory. The experiment results show that the proposed method can improve the quality of recommendation.