融合用户评分和属性相似度的协同过滤推荐算法

摘要

为了提高协同过滤推荐系统的推荐效率和准确性,更好地向用户提供个性化的推荐服务,提出一种用户评分和属性相似度的推荐算法.首先分析当前协同过滤推荐研究的现状,设计评分相似度、兴趣倾向相似度、置信度等作为评分标准,使得用户相似度的计算更加准确、有区分度,然后根据用户属性来衡量用户之间的相似度,最后利用MovieLens数据集和Book-Crossing数据集做对比试验,对比精度、通用性和不同稀疏度及冷启动情况下的性能.实验结果表明,本文算法不仅提高了推荐精度,而且明显优于其它协同过滤推荐算法,具有更高的实际应用价值.%In order to improve the of efficiency and accuracy of collaborative filtering recommendation,and provide personalized recommendation service to users,a novel collaborative filtering recommendation algorithm based on user score and user attributes similarity is proposed.Firstly,the similarity between the users is calculated according to the similarity of user scores,similarity of the user interest tendency,confidence.Secondly,the similarity between users is measured based on user attributes.Finally,the paper uses MovieLens data set and Book-Crossing data set to do comparative test,such as comparing precision,versatility and performance in different sparsity degree and cold start condition.The result shows that the proposed algorithm not only can improve the recommendation accuracy,but also is better than other collaborative filtering algorithms,and it has higher practical application value.