[目的/意义]微博对用户获取信息和建立社交网络具有重要作用。提出一种基于相似度和信任度融合的微博内容推荐方法，能够从用户需求出发进行个性化微博内容推荐，对提高微博服务质量、改善信息过载问题具有意义。[方法/过程]基于相似度和信任度融合算法，构建微博内容推荐模型，以新浪微博为研究对象，采用编程方式获取汽车、体育、运动健身、互联网和财经5个领域的数据，展开用户相似度与信任度计算的实验分析和比较。[结果/结论]分析结果显示该方法可以有效表示和挖掘微博内容，改善微博推荐的准确性和用户满意度。
[Purpose/significance] Micro-blog plays an important role in getting information and building social networks. In order to improve the quality of micro-blog services and improve the information overload, this paper proposes a recommendation method for micro-blog content, which can carry out personalized microblog content recommendation based on user demands. [Method/process] Based on the fusion algorithm of similarity and trust degree, this paper built a model of Micro-blog content recommendation. Then, it took sina micro-blog as the research object, used the programming way to get data from five areas of cars, sports, sports and fitness, internet and finance, and conducted comparative analysis on the basis of user similarity and trust calculation experiment. [Result/conclusion] The analysis results show that this method can effectively represent and mine micro-blog content, and improve the accuracy and user satisfaction of micro-blog recommendation.
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