{"id":{"repo_id":"wku-diss","oai_identifier":"oai:digitalcommons.wku.edu:theses-2253"},"canonical_url":"https://search.dev.ndltd.org/etd/wku-diss/oai:digitalcommons.wku.edu:theses-2253","repository":{"repo_id":"wku-diss","name":"Western Kentucky University","base_url":"https://digitalcommons.wku.edu/do/oai/"},"display":{"title":"A Hybrid Recommendation System Based on Association Rules","abstract":"<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>","abstract_html":"&lt;p&gt;Recommendation systems are widely used in e-commerce applications. The&lt;br /&gt;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.&lt;/p&gt;","abstract_has_math":false,"creators":["Alsalama, Ahmed"],"institution":null,"degree_name":"Master of Science","degree_level":null,"degree_discipline":"Department of Computer Science","degree_department":null,"school":null,"contributors":["Qi Li (Director), Guangming Xing, Zhonghang Xia"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-01T07:00:00Z","date_published":"2013-05-01T07:00:00Z","updated_at":"2026-07-24T06:08:16Z","subjects":["Artificial Intelligence","Data Mining","Recommender Systems","Computer Software","Information Retrieval","Electronic Commerce","Database Management","Systems Engineering","Computer Sciences","Databases and Information Systems","Systems Architecture"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.wku.edu/theses/1250","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Qi Li (Director), Guangming Xing, Zhonghang Xia"]},{"key":"dc:creator","label":"Author","values":["Alsalama, Ahmed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial Intelligence","Data Mining","Recommender Systems","Computer Software","Information Retrieval","Electronic Commerce","Database Management","Systems Engineering","Computer Sciences","Databases and Information Systems","Systems Architecture"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.wku.edu/theses/1250"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["<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>"]},{"key":"dc:title","label":"Title","values":["A Hybrid Recommendation System Based on Association Rules"]}]}],"canonical_facts":{"dc:contributor":["Qi Li (Director), Guangming Xing, Zhonghang Xia"],"dc:creator":["Alsalama, Ahmed"],"dc:description.abstract":["<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>"],"dc:identifier":["https://digitalcommons.wku.edu/theses/1250"],"dc:subject":["Artificial Intelligence","Data Mining","Recommender Systems","Computer Software","Information Retrieval","Electronic Commerce","Database Management","Systems Engineering","Computer Sciences","Databases and Information Systems","Systems Architecture"],"dc:title":["A Hybrid Recommendation System Based on Association Rules"],"dc:type":["Thesis"],"thesis:degree_discipline":["Department of Computer Science"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T06:08:16Z"}