TY - JFULL AU - Ahmed Mohammed K. Alsalama PY - 2015/2/ TI - A Hybrid Recommendation System Based On Association Rules T2 - International Journal of Computer and Information Engineering SP - 54 EP - 62 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10000147 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 97, 2015 N2 - Recommendation systems are widely used in e-commerce applications. The engine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose1 a hybrid framework recommendation system to be applied on two dimensional spaces (User × Item) with a large number of Users and a small number of Items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users. ER -