Become Popular in SNS: Tag Recommendation using FolkPopularityRank to Enhance Social Popularity

Toshihiko Yamasaki, Yiwei Zhang, Jiani Hu, Shumpei Sano, Kiyoharu Aizawa

Abstract: In this demo, we address two emerging yet challenging problems in social media: (1) scoring the text tags in terms of the influence to the numbers of views, comments, and favorite ratings of images and videos on content sharing services, and (2) recommending additional tags to increase such popularity-related numbers. For these purposes, we present a demo using our FolkPopularityRank (FP-Rank) algorithm, which can score and recommend text tags based on their ability to influence the popularity-related numbers. Our experiments using 1,000 photos showed that we can achieve 1.6 times more views than the original tag sets in Flickr just by adding tags recommended by FP-Rank.