Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
This addresses the need for fairer and more effective recommendations for users and creators on social platforms, representing an incremental advancement.
The paper tackles the problem of content recommendation in social media by proposing the Social Explorative Attention Network (SEAN) to benefit both creators and consumers, achieving improvements in recommendation equality measured by Gini coefficients and performance measured by F1 scores.
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.