  An effective content recommendation in modern social media platforms should
benefit both creators to bring genuine benefits to them and consumers to help
them get really interesting content. In this paper, we propose a model called
Social Explorative Attention Network (SEAN) for content recommendation. SEAN
uses a personalized content recommendation model to encourage personal
interests driven recommendation. Moreover, SEAN allows the personalization
factors to attend to users' higher-order friends on the social network to
improve the accuracy and diversity of recommendation results. Constructing two
datasets from a popular decentralized content distribution platform, Steemit,
we compare SEAN with state-of-the-art CF and content based recommendation
approaches. Experimental results demonstrate the effectiveness of SEAN in terms
of both Gini coefficients for recommendation equality and F1 scores for
recommendation performance.
