# M3TR: Temporal Retrieval Enhanced Multi-Modal Micro-video Popularity Prediction

arXiv: https://arxiv.org/abs/2411.15455 · HTML: https://mlsanity.com/paper/2411.15455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/1907.12303/) (2019)
- [A Content-Driven Micro-Video Recommendation Dataset at Scale](https://mlsanity.com/paper/2309.15379/) (2023)
- [Accelerating Large-Scale Inference with Anisotropic Vector Quantization](https://mlsanity.com/paper/1908.10396/) (2019)
- [Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework](https://mlsanity.com/paper/2003.12724/) (2020)
- [Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media](https://mlsanity.com/paper/2105.08809/) (2021)
- [Forecasting Popularity of Videos using Social Media](https://mlsanity.com/paper/1403.5603/) (2014)

## Built on by (most cited first, 1 of 1)

- [Evidence-Unit Fairness and the Limits of Query-Adaptive Sparse-Dense Fusion in Financial Document Retrieval](https://mlsanity.com/paper/2608.00183/) (2026)

## Abstract (arXiv)

Accurately predicting the popularity of micro-videos is a critical but challenging task, characterized by volatile, `rollercoaster-like' engagement dynamics. Existing methods often fail to capture these complex temporal patterns, leading to inaccurate long-term forecasts. This failure stems from two fundamental limitations: ding172 a superficial understanding of user feedback dynamics, which overlooks the mutually exciting and decaying nature of interactions such as likes, comments, and shares; and~ding173 retrieval mechanisms that rely solely on static content similarity, ignoring the crucial patterns of how a video's popularity evolves over time. To address these limitations, we propose M3TR, a Temporal Retrieval enhanced Multi-Modal framework that uniquely synergizes fine-grained temporal modeling with a novel temporal-aware retrieval process for Micro-video popularity prediction. At its core, M3TR introduces a Mamba-Hawkes Process (MHP) module to explicitly model user feedback as a sequence of self-exciting events, capturing the intricate, long-range dependencies within user interactions (for limitation ding172). This rich temporal representation then powers a temporal-aware retrieval engine that identifies historically relevant videos based on a combined similarity of both their multi-modal content (visual, audio, text) and their popularity trajectories (for limitation ding173). By augmenting the target video's features with this retrieved knowledge, M3TR achieves a comprehensive understanding of prediction. Extensive experiments on two real-world datasets demonstrate the superiority of our framework. M3TR achieves state-of-the-art performance, outperforming previous methods by up to 19.3% in nMSE and showing significant gains in addressing long-term prediction challenges.