Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data

• Main Authors: Najafabadi, M. K., Mahrin, M. N., Chuprat, S., Sarkan, H. M.
• Format: Article
• Published: Elsevier Ltd 2017
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/75957/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995610183&doi=10.1016%2fj.chb.2016.11.010&partnerID=40&md5=1724c1f18704634e5c43c28b74a18040
• Institution: Universiti Teknologi Malaysia

The recommender systems are recently becoming more significant in the age of rapid development of the Internet technology due to their ability in making a decision to users on appropriate choices. Collaborative filtering (CF) is the most successful and most applied technique in the design of recomme...