A Collaborative Filtering Model for Personalized Retweeting Prediction

Abstract

As the development of social media, the services in social media have significantly changed people’s habits of using Internet. However, as the large amount of information posted by users and the highly frequent updates in social media, users often face the problem of information overload and miss out of content that they may be interested in. Recommender systems, which recommends an item (e.g., a product, a service and a twitter etc.) to users based on their interests, is an effective technique to handle this issue. In this paper, we borrow matrix factorization model from recommender system to predict users’ behaviors of retweeting 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.