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Association Rule Mining in Collaborative Filtering
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Author(s): Carson K.-S. Leung (University of Manitoba, Canada), Fan Jiang (University of Manitoba, Canada), Edson M. Dela Cruz (University of Manitoba, Canada)and Vijay Sekar Elango (University of Manitoba, Canada)
Copyright: 2017
Pages: 21
Source title:
Collaborative Filtering Using Data Mining and Analysis
Source Author(s)/Editor(s): Vishal Bhatnagar (Ambedkar Institute of Advanced Communication Technologies and Research, India)
DOI: 10.4018/978-1-5225-0489-4.ch009
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Abstract
Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valuable to information users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF-Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering.
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