Collaborative Filtering Recommendation Algorithm Based on Time-Related Correlation Degree and Covering Degree
ZHANG Zhipeng1, ZHANG Yao2, REN Yonggong1
1.School of Computer and Information Technology, Liaoning Normal University, Dalian 116081 2.School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034
Abstract The traditional item-based collaborative filtering(IBCF) assigns equal weights to all items while computing similarity and prediction. And thus it cannot provide recommendations with high predictive accuracy and classification accuracy. Therefore, a time and covering weighting collaborative filtering(TCWCF) algorithm is proposed. A time-related correlation degree is applied to similarity computation to improve the predictive accuracy, and a covering degree is integrated into rating prediction to increase classification accuracy. Experimental results on MovieLens dataset suggest that TCWCF outperforms traditional IBCF and other algorithms and it provides recommendations with satisfactory predictive accuracy and classification accuracy for users.
Fund:Support by National Natural Science Foundation of China(No.61772252), Doctoral Start-up Foundation of Liaoning Normal University(No.BS2018L008)
About author:: ZHANG Zhipeng, Ph.D., lecturer. His research interests include data mining and recommender system.ZHANG Yao, Ph.D., lecturer. Her research interests include pattern recognition and simulation analysis.REN Yonggong(Corresponding author), Ph.D., professor. His research interests include artificial intelligence and data mining.
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2019, Vol. 32 
