PRES is a recommender system that recommends links (hyperlinks) based on content-based filtering. A content-based filtering system selects items based on the correlation between the content of the items and the user’s preferences. PRES makes recommendations by
comparing a user profile with the content of each document in the collection. Term weights of document can be determined by using the tf-idf scheme. The user profile is represented with the same terms and built up by analyzing the content of documents that the user found interesting. Finally similarity measures (cosine) are used to measure how similar is a document to user profile and those (links) unred are recommended to the user.
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