Become Popular in SNS: Tag Recommendation using FolkPopularityRank to Enhance Social Popularity
Become Popular in SNS: Tag Recommendation using FolkPopularityRank to Enhance Social Popularity
Toshihiko Yamasaki, Yiwei Zhang, Jiani Hu, Shumpei Sano, Kiyoharu Aizawa
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Demos. Pages 5252-5253.
https://doi.org/10.24963/ijcai.2017/781
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.
Keywords:
Machine Learning: Data Mining