Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Recommender systems,Collaborative filtering,Sparsity problem,Association rules mining,Clustering,Implicit feedback
论文评审过程：Received 12 September 2016, Revised 7 November 2016, Accepted 9 November 2016, Available online 16 November 2016, Version of Record 16 November 2016.
论文官网地址：https://doi.org/10.1016/j.chb.2016.11.010
