A User Adaptive Model for Followee Recommendation on Twitter

On the Twitter platform, an effective followee recommendation system is helpful to connecting users in a satisfactory manner. Topological relations and tweets content are two main factors considered in a followee recommendation system. However, how to combine these two kinds of information in a uniform framework is still an open problem. In this paper, we propose to combine deep learning techniques and collaborative information to explore the user representations latent behind the topology and content. Over two kinds of user representations (i.e., topology representation and content representation), we design an adaptive layer to dynamically leverage the contribution of topology and content to recommending followees, which changes the situation where the contribution weights are usually predefined. Experiments on a real-world Twitter dataset show that our proposed model provides more satisfying recommendation results than state-of-the-art methods.