  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.
