BPF++: A Unified Factorization model for predicting retweet behaviors

Abstract

Recently, the prediction of retweet behaviors has attracted significant attention, as it can facilitate with a number of tasks, such as popular tweet prediction, personalized recommendation and business intelligence. However, in existing studies, two main problems exists in the prediction of retweet behaviors. (1) The relationship between users is extremely simple when social influences are used for prediction. (2) An effective framework that unifies the effects of both heterogeneous social relations of users and multidimensional similarities of tweets does not exist. Therefore, we propose a unified factorization model that incorporates social influence and tweet similarity into a traditional Bayesian Poisson factorization (BPF) model, named BPF++. Specifically, we utilize a variety of social influence and tweet similarity jointly to improve performance. Furthermore, we integrate trust strengths between users and degrees of similarity between tweets to the framework. We adopt an efficient coordinate ascent algorithm to learn the parameters of the BPF++ model. Extensive experiments are conducted to evaluate the performance of our model on the Sina Weibo dataset. Results demonstrate improvements of 113.64% and 116.28% in the NDCG@3 and precision@3 scores, respectively, compared with BPF.