摘要
协同过滤算法有两个主要问题:新用户冷启动问题和相似用户的可靠性问题。为了解决上述问题,提出了基于内容和协同过滤相融合的推荐算法,主要解决新用户冷启动、相似用户可靠性问题。该算法的主要过程为,利用k-means聚类算法将数据集中的用户进行聚类,然后确定用户各个属性特征的适当权重,根据用户人口统计学特征的聚类方法,将新用户分配到恰当的类中,最后提取出新用户的最近邻,根据最近邻用户的项目评分,计算新用户对未评分项目的预评分,生成推荐列表。实验结果表明,在平均绝对误差(MAE)和均方根误差(RMAE)上有较明显的改善。
There are two main problems in collaborative filtering algorithm:the problem of new user cold start and the reliability of similar users. In order to solve the above problems, Recommendation Algorithm Based on the Combination of Content and Collaborative Filtering is proposed, which mainly solves the problem of cold start and similar user reliability. The main process of the algorithm is that the k-means clustering algorithm is used to cluster the users of the data set, then the appropriate weight of each attribute of the user is determined, and the new user is assigned to the appropriate class according to the clustering method of the user demographic characteristics, finally, the nearest neighbor of the new user is extracted. According to the project score of the nearest neighbor, Calculating the pre rating of a new user on a non rated project and generating a list of recommendations.The experimental results show that there is a significant improvement in the evaluation standard of MAE and RMSE.
出处
《电脑知识与技术》
2018年第1Z期232-234,282,共4页
Computer Knowledge and Technology
关键词
协同过滤
冷启动
人口统计学特征
K-MEANS聚类
混合推荐
collaborative filtering
cold start
demographic characteristics
k-means clustering
mixed recommended
