Advertisement recommendation on the Web is a popular research problem. For microblog platforms, different requirements arise due to the differences in the context of social media and social network. In this work, we propose an advertisement recommendation technique for microblogs. The proposed solution uses all contents of the messages (texts, captions, web links, hashtags), and enhances them with sentiment data and followee/follower interactions expressed as microblog posts to generate a new user model. As another novel feature, Wikipedia Good Pages are used as general background knowledge for matching user profiles and advertisement contents. On the basis of the similarity between advertisement vectors and user profile vectors, the most related advertisement for the selected user is determined. Evaluation results show that the proposed solution performs better for advertisement recommendation on microblog platform and works faster in comparison to other techniques.

中文翻译：

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维基百科利用情感增强用户档案丰富微博广告推荐

网络上的广告推荐是一个流行的研究问题。对于微博平台，由于社交媒体和社交网络的语境不同，产生了不同的要求。在这项工作中，我们提出了一种微博广告推荐技术。所提出的解决方案使用消息的所有内容（文本、标题、网络链接、主题标签），并通过情感数据和表达为微博帖子的关注者/关注者交互来增强它们，以生成新的用户模型。作为另一个新颖的特征，维基百科好页面被用作匹配用户资料和广告内容的一般背景知识。基于广告向量和用户简档向量之间的相似性，确定与所选用户最相关的广告。