计算机应用与软件2017，Vol.34Issue(4)：305-308,321,5.DOI:10.3969/j.issn.1000-386x.2017.04.052
融合用户评分和属性相似度的协同过滤推荐算法
A COLLABORATIVE FILTERING RECOMMENDATION ALGORITHM BASED ON USER SCORE AND ATTRIBUTE SIMILARITY
摘要
Abstract
In order to improve the recommendation efficiency and accuracy of collaborative filtering recommendation system, and to provide personalized recommendation service, a recommendation algorithm based on user score and attribute similarity is proposed.Firstly, the current status of collaborative filtering recommendation research is analyzed, and the similarity, similarity of interest tendency, confidence and other indicators are used as the scoring criteria, which makes the calculation of user similarity more accurate and discriminative.Then the similarity between users is measured according to the attributes of the users.The comparison is made between the MovieLens data set and the Book-Crossing data set, and the accuracy, versatility and performance under different sparsity and cold start conditions are compared.Experimental results show that the proposed algorithm not only improves the recommendation accuracy, but also is superior to other collaborative filtering recommendation algorithms, and has higher practical application value.关键词
推荐系统/协同过滤/相似性度量/稀疏性问题Key words
Recommendation system/Collaborative filtering/Similarity measurement/Sparsity problem分类
信息技术与安全科学引用本文复制引用
王三虎,王丰锦..融合用户评分和属性相似度的协同过滤推荐算法[J].计算机应用与软件,2017,34(4):305-308,321,5.基金项目
山西省教育厅教学改革项目(J2014120,J2015121). （J2014120,J2015121）
