Search

To provide the personalized services for learners according to user’s learning behavior, firstly, through theory of Fuzzy Ontology, the ambiguity and uncertainty of information in E-learning recommendation system was analyzed, and fuzzy information was extended to the ontology. Secondly, the user interest estimation based on behavior was studied in term of user’s learning preferences and cognitive status, user profile was described by the learning object, and then a user model updating algorithm was proposed. Finally, a fuzzy recommendation system based on user profile was built. As verified by experiment, the results have shown that the system can provided the personalized learning services according to user’s learning behavior, and it is useful in describing user preferences.

Aimed at the problem of the classical ontology cannot represent the imprecision and uncertainty information, firstly, the ambiguity and uncertainty of fuzzy information was analyzed, and the fuzzy concept relationship was expressed by using fuzzy membership function. And then, the user interest estimation based on behavior was studied in term of user’s learning preferences, and user profile was described by the learning object, Furthermore, fuzzy ontology under different granularity was built, the fuzzy concept lattice was clustered, and the concept similarity of fuzzy formal concepts was calculated. Finally, a fuzzy ontology framework based on user profile was proposed. As verified by experiment, the results have shown that the framework can reduce efficiently the uncertainty information of fuzzy ontology, and enhance the precision of ontology.