Learning using video media such as watching videos on YouTube is an alternative method of learning that often used. However, there are so many available finding with the right content difficult and time-consuming. Therefore, this study builds a recommendation system can recommend based courses syllabus. The works by looking for similarity between syllabus annotations cosine method. annotation t...

In this paper we present a hybrid approach that tries to alleviate the main item-based collaborative filtering (CF) drawback, the sparsity and the first-rater problem. By combining the contents of items into the item-based CF to find similar items and use the combined similarity to generate predictions. We describe and evaluate how similarity attributes can be discovered and exploited using ass...

Collaborative filtering and Content-based filtering methods are two famous methods used by recommender systems. Restricted Boltzmann Machine(RBM) model rivals the best collaborative filtering methods, but it focuses on modeling the correlation between item ratings. In this paper, we extend RBM model by incorporating content-based features such as user demograohic information, items categorizati...

To an organisation, centralized Internet filtering and blocking is very important for a couple of reasons. With the flooding of pornographic materials on the Web, educators and parents would like to block these offensive materials from their children. Companies also want to reduce the amount of work time that its employees spend on non-productive Web surfing. Current blocking and filtering mech...

Recommendation systems, although a well-studied topic, experience several shortcomings when applied on e-learning platforms. While collaborative filtering methods have enjoyed great success in making recommendations large scale e-commerce and social networking observation, users of platforms continually evolving preferences, which render weak. On the other end spectrum are content-based approac...