Personalized recommendation

Abstract: With vigorous development of the Internet, especially the web page interaction technology, distant E-learning has become more and more realistic and popular. Digital courses may consist of many learning units or learning objects and, currently, many learning objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant learning objects will be published and distributed cross the Internet. Facing huge volumes of learning objects, learners may be lost in selecting suitable and favorite learning objects. In this paper, an adaptive personalized recommendation model is proposed in order to help recommend SCORM-compliant learning objects from repositories in the Internet. This model adopts an ontological approach to perform semantic discovery as well as both preference-based and correlation-based approaches to rank the degree of relevance of learning objects to a learner’s intension and preference. By implementing this model, a tutoring system is able to provide easily and efficiently suitable learning objects for active learners

 Learning Object;  eLearning;  SCORM;  Personalized recommendation;  Learner Pattern;  Semantic Discovery;  LOM;  

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Abstract: With the advent of information technology, library services are facing tremendous changes in the form of digitalization. In addition to the digitalization of library resources, personalized systems and recommendation systems are two of highly desirable services among library patrons. This study proposes a novel recommendation system based on analysis of loan records. In our system, we use the traditional cataloging scheme, such as the Library of Congress Classification (LCC), as the reference ontology and build personal ontology by mining interested subjects and relationships among subjects from patron’s borrowing records. The proposed scheme can meet diversified demands of individual patron and provide patrons with a user-friendly interface to help them access needed information. Keywords: personalized service, personal ontology, information filtering, recommendation system.

 ontology;  Personalized recommendation;  

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