  We focus on the problem of streaming recommender system and explore novel
collaborative filtering algorithms to handle the data dynamicity and complexity
in a streaming manner. Although deep neural networks have demonstrated the
effectiveness of recommendation tasks, it is lack of explorations on
integrating probabilistic models and deep architectures under streaming
recommendation settings. Conjoining the complementary advantages of
probabilistic models and deep neural networks could enhance both model
effectiveness and the understanding of inference uncertainties. To bridge the
gap, in this paper, we propose a Coupled Variational Recurrent Collaborative
Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to
handle the streaming recommendation problem. The framework jointly combines
stochastic processes and deep factorization models under a Bayesian paradigm to
model the generation and evolution of users' preferences and items'
popularities. To ensure efficient optimization and streaming update, we further
propose a sequential variational inference algorithm based on a cross
variational recurrent neural network structure. Experimental results on three
benchmark datasets demonstrate that the proposed framework performs favorably
against the state-of-the-art methods in terms of both temporal dependency
modeling and predictive accuracy. The learned latent variables also provide
visualized interpretations for the evolution of temporal dynamics.
