基于事件社会网络（Event-Based Social Network，EBSN）是一种结合了线上网络和线下网络的新型社会网络，近年来得到了越来越多关注，已有许多国内外重要研究机构的研究者们对其进行研究并取得许多研究成果.在EBSN推荐系统中，一个重要任务就是设计出更好、更合理的推荐算法以提高推荐精确度和用户满意度，其关键在于充分结合EBSN中的各种上下文信息去挖掘用户、事件和群组的隐藏特征.本文主要对EBSN推荐系统的最新研究进展进行综述. 首先，概述EBSN的定义、结构、属性和特征，介绍EBSN推荐系统的基本框架，以及分析EBSN推荐系统与其他推荐系统的区别.其次，对EBSN推荐系统的主要推荐方法和推荐内容进行归纳、总结和对比分析.最后，分析EBSN推荐系统的研究难点及其发展趋势，并对本文作出总结.
We present a new method for jointly modelling the students' results in the university's admission exams and their performance in subsequent courses at the university. The case considered involved all the students enrolled at the University of Campinas in 2014 to evening studies programs in educational branches related to exact sciences. We collected the number of attempts used for passing the university course of geometry and the results of the admission exams of those students in seven disciplines. The method introduced involved a combination of multivariate generalised linear mixed models (GLMM) and graphical models for representing the covariance structure of the random components. The models we used allowed us to discuss the association of quantities of very different nature. We used Gaussian GLMM for modelling the performance in the admission exams and a frailty discrete-time Cox proportional model, represented by a GLMM, to describe the number of attempts for passing Geometry. The analyses were stratified into two populations: the students who received a bonus giving advantages in the university's admission process to compensate social and racial inequalities and those who did not receive the compensation. The two populations presented different patterns. Using general properties of graphical models, we argue that, on the one hand, the predicted performance in the admission exam of Mathematics could solely be used as a predictor of the performance in geometry for the students who received the bonus. On the other hand, the Portuguese admission exam's predicted performance could be used as a single predictor of the performance in geometry for the students who did not receive the bonus.