The idea of Content-based recommender system in an Elearning platform can be summarized as follows: Given the
lectures that the learner has visited, the platform recommends
other lectures with content that are similar to the content of
the viewed lectures. Since our approach is based on a search
engine based recommender system, the content of each lecture
is considered as a document and the recommendation of pages
is related to the matching between a learner’s query and the
reverse-indexing of the lecture (Webpage). Our Search engine
uses the Vector Space Model and the score of a query q
for a document d is computed based on the cosine-distance
similarity between the document and the query vector.