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

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• 作者：Maryam Khanian Najafabadi ; ^{knmaryam2@live.utm.my} ; ^{maryam64266@yahoo.com} ; Mohd Naz'ri Mahrin ; Suriayati Chuprat ; Haslina Md Sarkan
• 关键词：Recommender systems ; Collaborative filtering ; Sparsity problem ; Association rules mining ; Clustering ; Implicit feedback
• 刊名：Computers in Human Behavior
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：67
• 期：Complete
• 页码：113-128
• 全文大小：1618 K
• 卷排序：67

文摘

Tackle data sparsity using clustering and association rules mining on massive data. Utilizing users' implicit interaction records with items for improving CF. Using item repetition in a transaction as the input for association rules. Experiments show that the proposed technique substantially outperforms basic CF. Comparing the accuracy of proposed technique with other extended version of CF.