  In this paper we propose a method that can enhance the social popularity of a
post (i.e., the number of views or likes) by recommending appropriate hash tags
considering both content popularity and user popularity. A previous approach
called FolkPopularityRank (FP-Rank) considered only the relationship among
images, tags, and their popularity. However, the popularity of an image/video
is strongly affected by who uploaded it. Therefore, we develop an algorithm
that can incorporate user popularity and users' tag usage tendency into the
FP-Rank algorithm. The experimental results using 60,000 training images with
their accompanying tags and 1,000 test data, which were actually uploaded to a
real social network service (SNS), show that, in ten days, our proposed
algorithm can achieve 1.2 times more views than the FP-Rank algorithm. This
technology would be critical to individual users and companies/brands who want
to promote themselves in SNSs.

Social and Information Networks;Multimedia