  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.
