摘要： 通过分析微博特点及现有微博推荐发现算法的缺陷,提出一种新的微博推荐方法。采用标签检索策略对未加标签和标签较少的用户进行加标,构建用户-标签矩阵,得到用户-标签权重并利用标签信息表征用户兴趣。为解决该矩阵中高维稀疏的问题,通过挖掘标签间的关联关系,继而更新用户-标签矩阵,获得最终的用户兴趣并进行相关推荐。实验结果表明,与忽略标签间关系的微博推荐方法相比,该推荐方法能够更有效地进行微博推荐。

关键词: 微博推荐, 标签检索, 用户-标签矩阵, 用户-标签权重, 标签关联关系

Abstract: A microblog recommendation method based on label correlation relationship is presented via analyzing mircoblog features and the deficiencies of existing microblog recommendation finding algorithms.Label retrieval strategy is adopted to add label for unlabeled users and users with small number of labels,and the user-label matrix is built and user-label weights are obtained.The label information is used to represent user interests.In order to solve the problem of high-dimension sparsity of the matrix,the correlation relationship between the labels is investigated to update the user-label matrix so as to get the final user interests.Experimental results show that compared with unlabeled microblog recommendation algorithm,the presented recommendation method is more effective in microblog information recommendation.

Key words: microblog recommendation, label retrieval, user-label matrix, user-label weight, label correlation relationship

中图分类号: 

• TP391