摘要
随着社会互联网信息呈爆炸式增长,电影资源亦成为互联网资源的重要组成部分。传统的推介是基于受欢迎程度、销售、广告和其他方式的,不能向用户提供人性化和个性化的推介服务。因此,针对K-means聚类算法和SVD算法进行优化,并提出了改进算法。实验结果表明,通过K均值算法和SVD算法的优化提升了协同过滤算法的推荐性能。通过设计的实验与其他方法进行对比,达到了预设的研究目标。
Today’s society is the explosive growth of Internet information,and movie resources are an important part of Internet resources.Traditional referrals are based on popularity,sales,advertising and other methods,but they cannot provide users with humanized and personalized referral services.Therefore,this paper introduces K-means clustering algorithm and SVD algorithm to optimize,and proposes an improved algorithm.The experimental results show that the optimization of the K-means algorithm and the SVD algorithm improves the recommendation performance of the collaborative filtering algorithm.In order to prove the effectiveness of the method proposed in this paper,the designed experiment was compared with other methods to achieve the preset research goal.
作者
信晓艺
XIN Xiaoyi(School of Mathematics and Big Data, Dezhou University, Dezhou 253000, China)
出处
《渭南师范学院学报》
2021年第11期87-93,共7页
Journal of Weinan Normal University
关键词
大数据
影音推荐系统
聚类
SVD
big data
audio-visual recommendation system
clustering
SVD
