摘要
协同过滤算法是一个在各领域广泛使用的启发式推荐算法,但传统协同过滤算法存在冷启动、数据稀疏性、用户分类精度低等问题.以协同过滤算法中重要的分类模型为切入点,对协同过滤算法进行改进.在选取分类算法方面,使用支持向量机算法与K最近邻算法进行模型融合,得到一个适用于协同过滤模型的分类算法,用其代替传统协同过滤算法中的分类算法.实验结果表明,改进的个性化推荐算法模型能较好解决传统协同过滤算法存在的问题,在对用户喜好的推荐精度上有明显优化作用.
Collaborative Filtering Algorithm is a heuristic recommendation algorithm widely used in various fields.However,the traditional collaborative filtering algorithm has the problems of cold start,data sparsity and low user classification accuracy.Taking the important classification model in the collaborative filtering algorithm as a penetration point,the collaborative filtering algorithm was improved.For classification algorithms,the support vector machine algorithm and the K nearest neighbor algorithm was used to take model fusion,a classification algorithm for collaborative filtering models was obtained,which substitutes for the classification algorithm in traditional collaborative filtering algorithm.The experimental results show that the proposed personalized recommendation algorithm model solves the problems of traditional collaborative filtering algorithm and plays an obvious optimization role in the recommendation accuracy of user preferences.
作者
范波
李金曈
白天
许鹏程
FAN Bo;LI Jintong;BAI Tian;XU Pengcheng(School of Information Science and Engineering,Hunan Institute of Science and Technology,Yueyang 414006,China)
出处
《湖南理工学院学报（自然科学版）》
CAS
2021年第3期9-12,共4页
Journal of Hunan Institute of Science and Technology(Natural Sciences)
基金
湖南省大学生创新创业训练计划项目(湘教通[2020]191号)。
关键词
机器学习
协同过滤
支持向量机
K最近邻算法
machine learning
collaborative filtering
support vector machine
K nearest neighbor algorithm
