个性化微博信息流推荐技术研究-计算机应用技术专业论文.docxVIP

个性化微博信息流推荐技术研究 个性化微博信息流推荐技术研究 兰州交通大学硕士学位论文 兰州交通大学硕士学位论文 万方数据 万方数据 万方数据 万方数据 关键词：微博推荐；信息检索；主题模型；协同过滤；个性化；冷启动 论文类型：应用研究 II - Abstract Millions of people turn to microblogging services to gather real-time news or opinion about people, things, or events of interest, because it is very convenient and fast for users. As microblogging grows in popularity in recent years, microblogging services are becoming the key role of social networks to support information sharing. However, more and more users today are facing the challenge of information overloading. So, how to recommend the interesting and important tweets for user is the key issue. The main challenges of the personalized tweets stream recommendation is the contradiction between the small size of the microblog messages and the social features of microblogs and therefore how to detect personal interest exactly is the key problem in this thesis. Most of the current researches on the personalized tweets stream recommendation perform badly because of the limitations of models research and the challenges of microblogs data itself. In this thesis, we integrate the TF-IDF (term frequency–inverse document frequency) models of the similarity between streams of tweets and the similarity scores between streams of tweets based on LDA (Latent Dirichlet Allocation) models to solve this problem. The research contents are as follows: Firstly, an approach based on TF-IDF is proposed. In order to capture personal interests, the approach improves the TF-IDF model by combining the weight of single terms and pairs of terms and evaluates the similarity between the set of user ’s tweets and the stream of tweets coming to users based on the idea of collaborative filtering. Moreover, the model studies the cold-start problems and personal features of users to optimize the queue of the tweets received by users. Secondly, this thesis also borrows the machinery of latent variable topic models like the popular unsupervised model LDA which have b