基于多源信息相似度的微博用户推荐算法

针对传统的协同过滤（CF）推荐算法中存在的数据稀疏性和推荐准确率不高的问题，提出了基于多源信息相似度的微博用户推荐算法（MISUR）。首先，根据微博用户的标签信息运用K最近邻（KNN）算法对用户进行分类；然后，对得到的每个类中的用户分别计算其多源信息（微博内容、交互关系和社交信息）的相似度；其次，引入时间权重和丰富度权重计算多源信息的总相似度，并根据其大小进行TOP-N用户推荐；最后，在并行计算框架Spark上进行实验。实验结果表明，MISUR算法与CF算法和基于多社交行为的微博好友推荐算法（MBFR）相比，在准确率、召回率和效率方面都有较大幅度的提升，说明了MISUR算法的有效性。

Micro blog user recommendation algorithm based on similarity of multi-source information

Focusing on the data sparsity and low accuracy of recommendation existed in traditional Collaborative Filtering (CF) recommendation algorithm, a micro blog User Recommendation algorithm based on the Similarity of Multi-source Information, named MISUR, was proposed. Firstly, the micro blog users were classified by K-Nearest Neighbor (KNN) algorithm according to their tag information. Secondly, the similarity of the multi-source information, such as micro blog content, interactive relationship and social information, was calculated for each user in each class. Thirdly, the time weight and the richness weight were introduced to calculate the total similarity of multi-source information, and the TOP-N recommendation was used in a descending order. Finally, the experiment was carried out on the parallel computing framework Spark. The experimental results show that, compared with CF recommendation algorithm and micro blog Friend Recommendation algorithm based on Multi-social Behavior (MBFR), the superiority of the MISUR algorithm is validated in terms of accuracy, recall and efficiency.