A personalized hashtag recommendation approach using LDA-based topic model in microblog environment

With wide use of cloud computing technologies, microblog is used more widely for services providing more personal communities by user information sharing, dissemination and acquisition. In Microblog environment, hashtag is used to find messages with a specific theme or content, which can greatly facilitate information diffusion, microblog searching, event detection and topic analysis, etc. Recommending relevant hashtags to users in the cloud is challenging, because hashtags are created at tremendous speed alongside microblogs, and scattered in micro-blogging systems without a systematic organization. In this paper, a personalized hashtag recommendation approach is proposed according to the latent topical information in microblogs. With users represented by user-topics distribution, the proposed approach finds top-k similar users, then computes all hashtags' frequencies appeared in these users, and finally the most relevant hashtags are recommended to user. In order to excavate latent topical information, a Latent Dirichlet Allocation (LDA)-based topic model is also proposed, named Hashtag-LDA, which can greatly enhance the influence of hashtags on latent topics' generation by jointly modeling hashtags and words in microblogs. Hashtag-LDA can not only find meaningful latent topics, but also find global hashtags and the relationships between topics and hashtags. The experimental results on real Twitter dataset show that the proposed recommendation approach outperforms the related methods and Hashtag-LDA is effective.