一种基于标签概率相关性的微博推荐方法

向微博用户推荐对其有价值和感兴趣的内容,是改善用户体验的重要途径。通过分析微博的特点以及现有微博推荐算法的缺陷,利用标签信息表征用户兴趣,提出一种基于标签概率相关性的微博推荐方法 LPCMR。首先,该方法利用标签之间的概率相关性,构造标签相似性矩阵。然后通过相关性标签权重加权方案,加强标签权重,构建用户-标签矩阵。针对用户标签矩阵稀疏的问题,采用标签相似性矩阵对用户-标签矩阵进行更新,使该矩阵既包含用户兴趣信息,又包含标签与标签之间的关系。以新浪微博公开API抓取的微博信息作为实验数据,进行了一系列的实验和分析,结果表明本文提出的推荐算法具有较好的效果。

A microblog recommendation method based on label probability correlation

In order to improve users’ experience, it is necessary to recommend valuable and interesting contents for users. We propose a label probability correlation based microblog recommendation method (LPCMR）via analyzing microblog features and the deficiencies of existing microblog recommendation algorithms. Firstly, our method takes advantage of the probability correlation between tags to construct the label similarity matrix. Then the weight of the tag for each user is enhanced based on the relevance weighting scheme, and the user tag matrix is constructed. The matrix is updated using the label similarity matrix, which contains both the user interest information and the relationship between tags. Experimental results show that the algorithm is effective in microblog recommendation.