Abstract
A recommendation or a suggestion system is a branch of information filtering systems that aims to anticipate a user's liking of a specific product. The recommendation system mainly suggests a list of recommendations in an industry, using one of two methods: collaborative filtering or content-based filtering. In this paper, we propose an intelligent recommender system that aims to facilitate the process of academic advising. Our framework is built on a combination of association rules mining and a content-based filtering approach. Apriori algorithm (a well-known algorithm for mining frequent product sets and relevant association rule) is used to extract association rules, depending on the assumption that the space is (student x course), where number of students is greater than 100 and the number courses is less than 55 courses. The extracted rules are used as inference rules to present semantic knowledge to the results, where accuracy is improved. A list of recommended courses is offered after using Jaccard similarity coefficient to measure the similarity between courses.