Micro-Influencer Recommendation by Multi-Perspective Account Representation Learning
Shaokun Wang, Tian Gan, Yu-An Liu, Jianlong Wu, Yuan Cheng, Liqiang Nie · IEEE Transactions on Multimedia · 2022
Influencer marketing is emerging as a new marketing method, changing the marketing strategies of brands profoundly. In order to help brands find suitable micro-influencers as marketing partners, the micro-influencer recommendation is regarded as an indispensable part of influencer marketing. However, previous works only focus on modeling theindividual imageof brands/micro-influencers, which is insufficient to represent the characteristics of brands/micro-influencers over the marketing scenarios. In this case, we propose a micro-influencer ranking joint learning framework which models brands/micro-influencers from the perspective ofindividual image,target audiences, andcooperation preferences. Specifically, to model accounts’individual image, we extract topics information and images semantic information from historical content information, and fuse them to learn the account content representation. We introducetarget audiencesas a new kind of marketing role in the micro-influencer recommendation, in which audiences information of brand/micro-influencer is leveraged to learn the multi-modal account audiences representation. Afterward, we build the attribute co-occurrence graph network to minecooperation preferencesfrom social media interaction information. Based on account attributes, thecooperation preferencesbetween brands and micro-influencers are refined to attributes’ co-occurrence information. The attribute node embeddings learned in the attribute co-occurrence graph network are further utilized to construct the account attribute representation. Finally, the global ranking function is designed to generate ranking scores for all brand-micro-influencer pairs from the three perspectives jointly. The extensive experiments on a publicly available dataset demonstrate the effectiveness of our proposed model over the state-of-the-art methods.