Rumor detection and stance classification are specialized areas in the field of Information Retrieval and Natural Language Processing. Judging the stances

Preference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF $$\_$$_ Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF $$\_$$_ Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.

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.