关键词: 微博, 短文本推荐, 主题模型, 自然语言处理, 信息安全

Abstract: Based on the collaborative filtering traditional recommendation system need to collect relevant data of user‘s interests and preferences, to a certain extent involved in the user's personal privacy, current information security and privacy protection is one of the hot field of data mining, in order to avoid disclosure of user information with to security issues, In this paper, we propose a new short text recommendation method based on sentential semantic structure model. We fist use topic model structure text-theme matrix to several subtopics. We employ sentential semantic structure model to extract semantic features to get sentential semantic features. Then LDA topic model fusing sentential semantic structure model is used to calculate the pairwise sentence similarities and construct the similarity matrix. Then we acquire sentential relationship features. At last, combining both sentential semantic features and relationship features, the most informative text are extracted from each subtopic. Experimental results demonstrate the improvement of our proposed framework, ROUGE-1 value is 31.388% while ROUGE-SU* value is 15.701% on compress ratio at 0.5%.The results indicate that introducing sentential semantic structure model can better understand sentential semantic and using both sentential semantic features and relationship features can also enrich the features representation.

Key words: microblog, short text recommendation, topic model, natural language processing, information security