  As an emerging type of user-generated content, micro-video drastically
enriches people's entertainment experiences and social interactions. However,
the popularity pattern of an individual micro-video still remains elusive among
the researchers. One of the major challenges is that the potential popularity
of a micro-video tends to fluctuate under the impact of various external
factors, which makes it full of uncertainties. In addition, since micro-videos
are mainly uploaded by individuals that lack professional techniques, multiple
types of noise could exist that obscure useful information. In this paper, we
propose a multimodal variational encoder-decoder (MMVED) framework for
micro-video popularity prediction tasks. MMVED learns a stochastic Gaussian
embedding of a micro-video that is informative to its popularity level while
preserves the inherent uncertainties simultaneously. Moreover, through the
optimization of a deep variational information bottleneck lower-bound (IBLBO),
the learned hidden representation is shown to be maximally expressive about the
popularity target while maximally compressive to the noise in micro-video
features. Furthermore, the Bayesian product-of-experts principle is applied to
the multimodal encoder, where the decision for information keeping or
discarding is made comprehensively with all available modalities. Extensive
experiments conducted on a public dataset and a dataset we collect from Xigua
demonstrate the effectiveness of the proposed MMVED framework.
