Which account will you follow? Recommending influential accounts on social media

Yiwei Zhang, Xueting Wang, Toshihiko Yamasaki

Abstract: In the age of social media, brands spend large part of their budget on social media marketing to promote their products. Finding potential followers for brands has become an immense business opportunity. On the other hand, recommending influential accounts (such as brands and influencers) to ordinary users (customers) can help users find content of their interest. Therefore, matching among influential accounts and ordinary users is a necessary task and could be a powerful marketing tool. In order to effectively calculate compatibility among influential accounts and ordinary users, we consider that hashtags posted by users somehow represent their preferences and could be useful resources. We collected two Instagram datasets: including a brand dataset consisting of 99 brands with 78,996 followers and an influencer dataset consisting of 80 influencers with 43,992 followers. We utilize these users and their posted hashtags to create an account-user-tag graph. We propose a novel framework that incorporates graph embedding and pairwise learning to rank for better recommendation. The random walk based graph embedding method can capture high-order proximity in the interaction data. But it ignores some parts in the graph due to its randomness. The pairwise learning to rank component is designed for the complementary purpose. Experimental results showed that the proposed method is effective at recommending influential accounts when compared with existing methods. In the top-10 recommendation task, our proposed method achieves hit ratio of 0.416 in the brand dataset and 0.524 in the influencer dataset.