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An effective content recommendation in modern social media platforms shouldbenefit both creators to bring genuine benefits to them and consumers to helpthem get really interesting content. In this paper, we propose a model calledSocial Explorative Attention Network (SEAN) for content recommendation. SEANuses a personalized content recommendation model to encourage personalinterests driven recommendation. Moreover, SEAN allows the personalizationfactors to attend to users' higher-order friends on the social network toimprove the accuracy and diversity of recommendation results. Constructing twodatasets from a popular decentralized content distribution platform, Steemit,we compare SEAN with state-of-the-art CF and content based recommendationapproaches. Experimental results demonstrate the effectiveness of SEAN in termsof both Gini coefficients for recommendation equality and F1 scores forrecommendation performance.

Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction