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Feature-based recommendations for one-to-one marketing
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Developing an ontology-supported information integration and recommendation system for scholars
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Collaboration recommendation on academic social networks
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| Hi-index | 12.06 |
Following tremendous advancement in information technology, the speed of information development has become increasingly fast-paced. Yet the overabundance of information has forced users to spend more time and resources in searching for information relevant to their needs. Today, recommendation systems already exist that provide services like filtering, customization, and others to assist users in searching for the right information. This study proposes to use ontology and the spreading activation model for research paper recommendation, hoping that it can elevate the performance of the recommendation system and also improve the shortcomings of today's recommendation systems. This study utilizes ontology to construct user profiles and makes use of user profile ontology as the basis to reason about the interests of users. Furthermore, this study takes advantage of the spreading activation model to search for other influential users in the community network environment, making a study on their interests in order to provide recommendation on related information. Based on actual experiment results, the method of ontology network analysis that combines ontology and the spreading activation model is effective in knowing the research interests of users. Hence, using the mechanism proposed in this study can make up for the insufficiencies or shortcomings of other recommendation systems. Moreover, the precision rate can be up to 93% showing that our recommendation system has a positive effect on the effectiveness of the recommendation.