# Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Framework

arXiv: https://arxiv.org/abs/2107.13943 · HTML: https://mlsanity.com/paper/2107.13943/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks](https://mlsanity.com/paper/2011.07267/) (2020)
- [Multi-domain Dialog State Tracking using Recurrent Neural Networks](https://mlsanity.com/paper/1506.07190/) (2015)
- [Extending Unsupervised Neural Image Compression With Supervised Multitask Learning](https://mlsanity.com/paper/2004.07041/) (2020)
- [Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network](https://mlsanity.com/paper/1406.3474/) (2014)
- [Stochastic Top-k ListNet](https://mlsanity.com/paper/1511.00271/) (2015)

## Built on by (most cited first, 2 of 2)

- [The Case for a Legal Compliance API for the Enforcement of the EU's Digital Services Act on Social Media Platforms](https://mlsanity.com/paper/2205.06666/) (2022)
- [Computational Studies in Influencer Marketing: A Systematic Literature Review](https://mlsanity.com/paper/2506.14602/) (2025)

## Abstract (arXiv)

With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased. Brands are looking for improved ways to identify valuable influencers among a vast catalogue; this is even more challenging with "micro-influencers", which are more affordable than mainstream ones but difficult to discover. In this paper, we propose a novel multi-task learning framework to improve the state of the art in micro-influencer ranking based on multimedia content. Moreover, since the visual congruence between a brand and influencer has been shown to be good measure of compatibility, we provide an effective visual method for interpreting our models' decisions, which can also be used to inform brands' media strategies. We compare with the current state-of-the-art on a recently constructed public dataset and we show significant improvement both in terms of accuracy and model complexity. The techniques for ranking and interpretation presented in this work can be generalised to arbitrary multimedia ranking tasks that have datasets with a similar structure.