  In modern social media platforms, an effective content recommendation should
benefit both creators to bring genuine benefits to them and consumers to help
them get really interesting content. To address the limitations of existing
methods for social recommendation, we propose Social Explorative Attention
Network (SEAN), a social recommendation framework that uses a personalized
content recommendation model to encourage personal interests driven
recommendation. SEAN has two versions: (1) SEAN-END2END allows user's attention
vector to attend their personalized interested points in the documents. (2)
SEAN-KEYWORD extracts keywords from users' historical readings to capture their
long-term interests. It is much faster than the first version, more suitable
for practical usage, while SEAN-END2END is more effective. Both versions allow
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 in two languages, English and Spanish, from a popular
decentralized content distribution platform, Steemit, we compare SEAN models
with state-of-the-art collaborative filtering (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 accuracy.
