The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and high-velocity, which poses tremendous challenges to traditional recommender systems. In this paper, we investigate the problem of recommendation with stream inputs. In particular, we provide a principled framework termed sRec, which provides explicit continuous-time random process models of the creation of users and topics, and of the evolution of their interests. A variational Bayesian approach called recursive meanfield approximation is proposed, which permits computationally efficient instantaneous on-line inference. Experimental results on several real-world datasets demonstrate the advantages of our sRec over other state-of-the-arts.

翻译：现实世界推荐人系统越来越受欢迎,不断和迅速地生成数据,在数据流情景下研究推荐人系统更为现实。数据流具有不同的特性,如时间顺序、连续和高速,对传统推荐人系统构成巨大挑战。在本文件中,我们用流输入调查建议问题。特别是,我们提供了一个称为SRec的原则框架,它提供了建立用户和专题及其利益演变的明确、连续、随机的过程模型。提出了一种称为回溯式平均场近似的变异方法,它允许进行高效的瞬时在线推断。几个现实世界数据集的实验结果显示了我们SRec相对于其他状态的优势。