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JST Press Release

August 1, 2017
Japan Science and Technology Agency (JST)
5-3, Yonbancho, Chiyoda-ku, Tokyo 102-8666

AI Generates More “Attractive” Hash Tags

Tag Recommendation for Enhancing Social Popularity in Content Sharing Services

Prof. Toshihiko Yamasaki, Associate Professor of The University of Tokyo, has developed an AI algorithm that can score text tags based on their ability to influence the popularity-related numbers such as the number of views in Social Networking Services (SNSs). 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. Experimental results showed that images using the recommended tags along with the original human-annotated tags can achieve twice the number of views as compared to those only with human-annotated tags.

Program Information

Research Area “Development and Integration of Artificial Intelligence Technologies for Innovation Acceleration”
Research Theme “Experience and Action Sensing of Media Consumers based on Unknown Target Retrieval and Recognition Framework”

Journal Information

Toshihiko Yamasaki, Jiani Hu, Shumpei Sano and Kiyoharu Aizawa, “FolkPopularityRank: Tag Recommendation for Enhancing Social Popularity using Text Tags in Content Sharing Services”, Proceedings of the 26th International Joint Conference on Artificial Intelligence, doi: 10.24963/ijcai.2017/451


[About Research]
Toshihiko YAMASAKI, Associate Professor
Department of Information and Communication Engineering,
Graduate School of Information Science and Technology, The University of Tokyo

[About Program]
Koji Matsuo
ICT Group, Department of Innovation Research, JST


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