Computer Engineering ›› 2020, Vol. 46 ›› Issue (3): 73-78,86.doi: 10.19678/j.issn.1000-3428.0054223

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

Improved Mini Batch K-Means Time-weighted Recommendation Algorithm

XU Huijun, WANG Zhong, MA Liping, RAO Hua, HE Cheng'en   

  1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China
  • Received:2019-03-14 Revised:2019-05-07 Published:2019-06-06

Abstract: The traditional collaborative filtering algorithm has the problems of sparse data,weak scalability and deviated user interest,causing low efficiency in algorithm operation and low accuracy in prediction.To address these problems,this paper proposes an improved Mini Batch K-Means time-weighted recommendation algorithm.The Pearson correlation coefficient is used to improve the Mini Batch K-Means clustering,and the improved clustering algorithm is applied to cluster the sparse scoring matrix,calculate user interest score and complete the filling of sparse matrix.Giving the influence of user interest varying with time,this paper introduces the Newton's law of cooling time weight to improve the similarity.The filled scoring matrix is used to perform weighted calculation on the similarity and on this basis,the final score is obtained.Experimental results show that compared with the traditional collaborative filtering algorithm,the mean absolute error of the proposed algorithm is reduced by 31.08%,and the precision,recall and F1 value are improved a lot,which shows its high scoring prediction accuracy.

Key words: collaborative filtering, predictive filling, Pearson correlation coefficient, Mini Batch K-Means clustering, Newton's law of cooling

CLC Number: