Discover Micro-Influencers for Brands via Better Understanding

TLDR

Influencer marketing has expanded rapidly, and micro‑influencer recommendation has become a key sub‑task, yet campaigns must address more than just marketing effectiveness. The study proposes a concept‑based micro‑influencer ranking framework to simultaneously improve marketing effectiveness and meet self‑development needs. The framework represents accounts concept‑wise, learns endorsement effect and influence scores, and uses a bi‑directional concept attention mechanism to rank micro‑influencers with interpretable parameters. Experiments on a real‑world dataset show that the proposed method outperforms state‑of‑the‑art approaches.

Abstract

With the rapid development of the influencer marketing industry in recent years, the cooperation between brands and micro-influencers on marketing has achieved much attention. As a key sub-task of influencer marketing, micro-influencer recommendation is gaining momentum. However, in influencer marketing campaigns, it is not enough to only consider marketing effectiveness. Towards this end, we propose a concept-based micro-influencer ranking framework, to address the problems of marketing effectiveness and self-development needs for the task of micro-influencer recommendation. Marketing effectiveness is improved by concept-based social media account representation and a micro-influencer ranking function. We conduct social media account representation from the perspective of historical activities and marketing direction. And two adaptive learned metrics, endorsement effect score and micro-influencer influence score, are defined to learn the micro-influencer ranking function. To meet self-development needs, we design a bi-directional concept attention mechanism to focus on brands' and micro-influencers' marketing direction over social media concepts. Interpretable concept-based parameters are utilized to help brands and micro-influencers make marketing decisions. Extensive experiments conducted on a real-world dataset demonstrate the advantage of our proposed method compared with the state-of-the-art methods.