Micro-Influencer Recommendation by Multi-Perspective Account Representation Learning
Shaokun Wang, Tian Gan, Yu-An Liu, Jianlong Wu, Yuan Cheng, Liqiang Nie
Abstract
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 the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual image</i> of 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 of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual image</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target audiences</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperation preferences</i> . Specifically, to model accounts’ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual image</i> , we extract topics information and images semantic information from historical content information, and fuse them to learn the account content representation. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target audiences</i> as 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 mine <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperation preferences</i> from social media interaction information. Based on account attributes, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperation preferences</i> between 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.