A Hybrid Recommendation System Based on Association Rules

<p>Recommendation systems are widely used in e-commerce applications. The<br />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.</p>

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Department of Computer Science

Year

2013

Author dc:creator

• Alsalama, Ahmed

Contributors dc:contributor

• Qi Li (Director), Guangming Xing, Zhonghang Xia

• Artificial Intelligence
• Data Mining
• Recommender Systems
• Computer Software
• Information Retrieval
• Electronic Commerce
• Database Management
• Systems Engineering
• Computer Sciences
• Databases and Information Systems
• Systems Architecture

Repository record dc:identifier

https://digitalcommons.wku.edu/theses/1250

OAI identifier oai:identifier

oai:digitalcommons.wku.edu:theses-2253