The SOM based Improved K-means Clustering Collaborative Filtering Algorithm in TV Recommendation System

摘要：

　　This paper aims on collaborative filtering(CF)in TV recommendation system which combines content-based and collaborative filtering recommendation mechanism,we propose an algorithm that using the self-organizing mapping(SOM)to optimize the improved k-means(IK)clustering in collaborative filtering.The whole clustering algorithm is divided into two phases: at the first stage,the quantity of the preliminary clustering and the central point of each cluster were acquired by means of the auto-clustering advantages of SOM algorithm; at the second stage,we first improve the basic k-means algorithm.We utilize the adjusted cosine similarity to calculate the distance from the user to the cluster center,and when calculating the mean of the cluster,just consider all of those users who have given scores for items.The improved k-means improves the accuracy of clustering and is more suitable for using in CF system compared to the basic k-means algorithm.Furthermore we take the results of SOM as the initial input of the IK algorithm to make the further clustering,the accurate clustering results are gained.Finally,the simulations verify that the MAE(mean absolute error)was reduced by 15.7%and 17.4%respectively compared to IK and k-means algorithms; the proposed approach increases the quality of clustering,and enhances the accuracy of the recommended TV program.

关键词： collaborative filtering clustering recommendation SOM k-means

作者: Zhaocai Ma Yi Yang Fei Wang Caihong Li Lian Li

作者单位: School of Information Science & Engineering Lanzhou University Lanzhou China

会议类型: 国际会议

会议名称: 2014 2nd International Conference on Advanced Cloud and Big Data (CBD 2014)(2014年先进云计算和大数据国际会议)

会议地点: 安徽黄山

会议语种:英文

页码: 288-295

在线出版日期: 2014-11-20（万方平台首次上网日期，不代表论文的发表时间）