FolkPopularityRank: Tag Recommendation for Enhancing Social Popularity using Text Tags in Content Sharing Services

FolkPopularityRank: Tag Recommendation for Enhancing Social Popularity using Text Tags in Content Sharing Services

Toshihiko Yamasaki, Jiani Hu, Shumpei Sano, Kiyoharu Aizawa

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3231-3237. https://doi.org/10.24963/ijcai.2017/451

In this study, 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 the FolkPopularityRank algorithm, which can score text tags based on their ability to influence the popularity-related numbers. The FolkPopularityRank algorithm is inspired by the PageRank and FolkRank algorithms but the scores of the tags are calculated not only by the co-occurrence of the tags but also by considering the popularity-related numbers of the content. To the best of our knowledge, this is the first attempt to recommending tags that can enhance popularity attributes of social media. We conducted extensive experiments with about 1,000 images. We uploaded the photos with the recommended tags along with the original tags to Flickr as a real test, and obtained very promising results.
Keywords:
Machine Learning: Data Mining