摘要
本文通过对K-means聚类算法和协同过滤推荐算法的学习研究。针对基于用户的协同过滤算法的不足,将改进的K-means聚类算法融入其中,设计了基于K-means聚类算法的个性化推荐算法,并将其应用于旅游景点及线路的个性化推荐中,以提高个性化推荐质量。实验结果表明,基于改进的K-means聚类的协同过滤算法缓解了初始数据的稀疏性问题,针对不同用户喜爱的旅游景点及线路推荐,在准确率和召回率两个方面证明可以提高个性化推荐的准确度。
This article is based on the study of K-means clustering algorithm and collaborative filtering recommendation algorithm. Aiming at the deficiencies of the user-based collaborative filtering algorithm, the improved K-means clustering algorithm is incorporated into it, and a personalized recommendation algorithm based on the K-means clustering algorithm is designed and applied to the personalized recommendation of tourist attractions and routes In order to improve the quality of personalized recommendations. The experimental results show that the collaborative filtering algorithm based on improved K-means clustering alleviates the sparsity problem of the initial data. It is proved that it can improve the personalization in terms of accuracy and recall rate for different users’ favorite tourist attractions and routes recommendation. Recommended accuracy.
作者
刘鑫
LIU Xin(Jilin Institute of Architecture and Technology,Changchun Jilin 130114)
出处
《软件》
2021年第3期97-99,共3页
Software
基金
吉林建筑科技学院2019年校级科研项目“基于聚类的推荐算法及应用研究”(项目编号:校科字[2019]012号)。
关键词
K-MEANS聚类
协同过滤算法
最小生成树
K-means clustering
collaborative fi ltering algorithm
minimum spanning tree
