Matrix factorization from a small number of observed entries has recently
garnered much attention as the key ingredient of successful recommendation
systems. One unresolved problem in this area is how to adapt current methods to
handle changing user preferences over time. Recent proposals to address this
issue are heuristic in nature and do not fully exploit the time-dependent
structure of the problem. As a principled and general temporal formulation, we
propose a dynamical state space model of matrix factorization. Our proposal
builds upon probabilistic matrix factorization, a Bayesian model with Gaussian
priors. We utilize results in state tracking, such as the Kalman filter, to
provide accurate recommendations in the presence of both process and
measurement noise. We show how system parameters can be learned via
expectation-maximization and provide comparisons to current published
techniques.

提出一种基于状态空间模型的矩阵分解方法，可解决处理用户随时间变化的推荐系统问题，利用卡尔曼滤波器等技术提供准确的推荐结果，通过期望最大化算法学习系统参数，并与当前发表的技术进行比较。