A Tag Recommendation System for Popularity Boosting

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

Abstract: In order to support users in the tagging process and recommendation, we had proposed two tag ranking algorithms, Document Frequency-Weights from regression and Folk Popularity Rank, which can extract tags greatly influencing popularity. We have developed a tag recommendation system using the algorithm we proposed. The recommended tags are not only for appropriate annotations but also for popularity boosting.