Advisor(s) - Committee Chair
Qi Li (Director), Guangming Xing, Zhonghang Xia
Department of Computer Science
Master of Science
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 propose 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.
Computer Sciences | Databases and Information Systems | Systems Architecture
Alsalama, Ahmed, "A Hybrid Recommendation System Based on Association Rules" (2013). Masters Theses & Specialist Projects. Paper 1250.