Abstract
In the learning environments, users would be helpless without the assistance of powerful searching and browsing tools to find their way. Web- based e- learning systems are normally used by a wide variety of learners with different skills, background, preferences, and learning styles.
In this paper, we perform the personalized semantic search and recommendation of learning contents on the learning Web-based environments to enhance the learning environment. Semantic and personalized search of learning content is based on a comparison of the learner profile that is based on learning style, and the learning objects metadata. This approach needs to present both the learner profile and the learning object description as certain data structures. Personalized recommendation of learning objects uses an approach to determine a more suitable relationship between learning objects and learning profiles. Thus, it may advise a learner with most suitable learning objects. Semantic learning objects search is based on the query expansion of the user query and by using the semantic similarity to retrieve semantic matched learning objects.