Proposed Recommender System for Open Educational Resources for Informatics Institute for Postgraduate Studies

finding user appropriate information among the enormous amount of data on the internet is a challenging method. Therefore, an information filtering method is required to assist users to discover their contents of interest. The Recommendation system is the most popular method used in many websites today to assist the process of creating suggestions. This paper will explain the design of an OER (Open Educational Resources) recommender system using KNN and Naïve Bayes classification algorithms, and present the evaluation of the results of their performance. The proposed system follows the content- based recommendation approach, by analyzing the user's actions, such as ratings and compare with resource features to create a user-profile model and generate an accurate recommendation.