Article ID Journal Published Year Pages File Type
462036 Journal of Systems and Software 2012 15 Pages PDF
Abstract

To refine user interest profiling, this paper focuses on extending scientific subject ontology via keyword clustering and on improving the accuracy and effectiveness of recommendation of the electronic academic publications in online services. A clustering approach is proposed for domain keywords for the purpose of the subject ontology extension. Based on the keyword clusters, the construction of user interest profiles is presented on a rather fine granularity level. In the construction of user interest profiles, we apply two types of interest profiles: explicit profiles and implicit profiles. The explicit profiles are obtained by relating users’ interest-topic relevance factors to users’ interest measurements of these topics computed by a conventional ontology-based method, and the implicit profiles are acquired on the basis of the correlative relationships among the topic nodes in topic network graphs. Three experiments are conducted which reveal that the uses of the subject ontology extension approach as well as the two types of interest profiles satisfyingly contribute to an improvement in the accuracy of recommendation.

► We create an ontology extension method through clustering weighted keyword graphs. ► We propose a user interest profiling refinement method. ► User interest profiles consist of explicit and implicit interest profiles. ► Subtle differences between two users’ interests on a topic can be detected.

Related Topics
Physical Sciences and Engineering Computer Science Computer Networks and Communications
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