Recommender systems are needed to find subject items of one’s interest. We review recommender systems and recommendation methods. We propose a subject personalization framework based on adaptive hypermedia for Computer Science ACM Curricula. We extend Hermes framework with subject recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Specialization and standard subject database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.