摘要： 传统的协同过滤算法存在数据稀疏、可扩展性弱和用户兴趣度偏移等问题，算法运行效率和预测精度偏低。针对上述问题，提出一种改进的Mini Batch K-Means时间权重推荐算法。采用Pearson相关系数改进Mini Batch K-Means聚类，利用改进的聚类算法对稀疏评分矩阵进行聚类，计算用户兴趣评分并完成对稀疏矩阵的填充。考虑用户兴趣随时间变化的影响，引入牛顿冷却时间权重计算相似度，并基于已填充评分矩阵进行相似度加权计算，得到项目最终评分。实验结果表明，与传统协同过滤算法相比，该算法的平均绝对误差下降了31.08%，准确率、召回率、F1值均有较大提升，具有较高的评分预测精确度和准确度。

关键词: 协同过滤, 预测填充, Pearson相关系数, Mini Batch K-Means聚类, 牛顿冷却定律

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

中图分类号: 

• TP18