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

Wenyi Xiao, Huan Zhao, Haojie Pan, Yangqiu Song, Vincent W. Zheng, Qiang Yang

Abstract:

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.