Abstract
Personalized recommendation is implemented by computing the similarity of user's interests and resources. Most of current recommendation systems compute the similarity based on keywords, which is simply implemented but much semantic information are lost. This paper takes the personalized recommendations of digital library as example, proposes a method to implement personalized recommendation service. User profile and resource features are represented by vector space model. And then these keyword vectors are extended in conceptual level by virtue of domain ontology and HowNet knowledge database. So conceptual space vectors of user and resource are generated. Therefore, personalized recommendation service is provided to user according to the similarity of the conceptual space vectors. Experiment data shows that the similarity based on concept is more efficient than similarity based on keyword in personalized recommendation service.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
