Collaborative Filtering Recommendation Algorithm Based on Time-Related Correlation Degree and Covering Degree

被引：0
|
作者：
Zhang Z. [1 ]
Zhang Y. [2 ]
Ren Y. [1 ]
机构：
[1] School of Computer and Information Technology, Liaoning Normal University, Dalian
[2] School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Classification Accuracy; Collaborative Filtering; Covering Degree; Predictive Accuracy; Time-Related Correlation Degree;
D O I：
10.16451/j.cnki.issn1003-6059.201904001
中图分类号：
学科分类号：
摘要：
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. 2019, Science Press. All right reserved.
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收藏
页码：289 / 297
页数：8
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