计算机工程与应用2018，Vol.54Issue(10)：105-109,212,6.DOI:10.3778/j.issn.1002-8331.1709-0083
基于相似度融合和动态预测的兴趣点推荐算法
Point of interest recommendation algorithm based on similarity integra-tion and dynamic prediction
摘要
Abstract
There are two problems in the existing POI recommendation algorithm. First, most algorithms mainly utilize the historical check-in data of user,while ignoring the comments of users and label information.Thus,the cold-start prob-lem cannot be solved effectively.Second,some algorithms only use the user's check-in score when calculating the similar-ity.The high sparseness of the POI check-in matrix results in the inaccurate ness of the recommendation.In view of the above problems,this paper uses the LDA topic model to mine the user's interest topic,and then integrates the check-in data for similarity measure to solve the cold-start problem. In recommendation period, a dynamic prediction method is proposed to dynamically fill the missing data and further alleviate the sparse data and improve the recommended quality. The experimental results on the real data set show that the proposed similarity fusion and dynamic prediction based recom-mend algorithm can effectively solve the problem of data sparseness and cold-start.The recommend performance is supe-rior to traditional recommendation algorithms.关键词
潜在的狄利克雷分配(LDA)主题模型/动态预测/兴趣点推荐/相似度Key words
Latent Dirichlet Allocation(LDA)topic model/dynamic prediction/point of interest recommendation/similarity分类
信息技术与安全科学引用本文复制引用
李心茹,夏阳,张硕硕..基于相似度融合和动态预测的兴趣点推荐算法[J].计算机工程与应用,2018,54(10):105-109,212,6.基金项目
国家自然科学基金(No.U1510115) （No.U1510115）
"青蓝工程"资助 （）
中国博士后科学基金特别资助项目(No.2013T60574). （No.2013T60574）
