As the development of social media, the services in social media have significantly changed people's habits of using Internet. How- ever, as the large amount of information posted by users and the highly frequent updates in social media, users often face the problem of informa- tion overload and miss out of content that they may be interested in. Rec- ommender systems, which recommends an item (e.g., a product, a service and a twitter etc.) to users based on their interests, is an effective tech- nique to handle this issue. In this paper, we borrow matrix factorization model from recommender system to predict users' behaviors of retweet- ing in social media. Compared with previous works, we take the relevance of users' interests, tweets' content, and publishers' influence into account simultaneously. Our experimental results on a real-world dataset show that the proposed model achieves desirable performance in characterizing users' retweeting behaviors and predicting topic diffusion in social media.
['Jun Li', 'Jiamin Qin', 'Tao Wang', 'Yi Cai', 'Huaqing Min']
A Collaborative Filtering Model for Personalized Retweeting Prediction