Time-Aware Social Hierarchical Poisson Factorization for Personalized Recommendation

Microblog is different from traditional social networks and e-commerce website for its low user activity, data sparsity and dynamic of users' preference. So traditional recommendation algorithms effect certainly is not very aridly ideal for Microblog. In this paper, we develop a time-aware social hierarchical Poisson factorization (HPF_TS) model to make personalized micro-blog recommendation to each user. HPF_TS is a new Bayesian factorization model based hierarchical Poisson factorization that accounts for socialization information, the time characteristics of user preferences to recommend micro-blogs to users whom are interested in. The interpret ability of the recommendation is also attached more importance to latent topics by utilizing gamma-Poisson structure for modeling items' content. We studied our models with four real-world data sets and the results show that the superior performance of the proposed model, compared with several alternative methods.