# Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework

arXiv: https://arxiv.org/abs/2003.12724 · HTML: https://mlsanity.com/paper/2003.12724/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Seeing Further and Wider: Joint Spatio-Temporal Enlargement for Micro-Video Popularity Prediction](https://mlsanity.com/paper/2604.20311/) (2026)
- [View while Moving: Efficient Video Recognition in Long-untrimmed Videos](https://mlsanity.com/paper/2308.04834/) (2023)
- [M3TR: Temporal Retrieval Enhanced Multi-Modal Micro-video Popularity Prediction](https://mlsanity.com/paper/2411.15455/) (2024)
- [Short-video Propagation Influence Rating: A New Real-world Dataset and A New Large Graph Model](https://mlsanity.com/paper/2503.23746/) (2025)
- [OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction](https://mlsanity.com/paper/2604.26252/) (2026)
- [Enhancing Social Media Post Popularity Prediction with Visual Content](https://mlsanity.com/paper/2405.02367/) (2024)
- [Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles](https://mlsanity.com/paper/2409.17287/) (2024)
- [Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction](https://mlsanity.com/paper/2606.27539/) (2026)

## Abstract (arXiv)

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.