{"type":"ScholarlyArticle","title":"FolkPopularityRank: Tag Recommendation for Enhancing Social Popularity using Text Tags in Content Sharing Services","authors":[{"name":"Toshihiko Yamasaki","slug":"toshihiko-yamasaki","given":"Toshihiko","family":"Yamasaki","affiliation":"Department of Information and Communication Engineering , The University of Tokyo"},{"name":"Jiani Hu","slug":"jiani-hu","given":"Jiani","family":"Hu","affiliation":"Department of Information and Communication Engineering , The University of Tokyo"},{"name":"Shumpei Sano","slug":"shumpei-sano","given":"Shumpei","family":"Sano","affiliation":"Department of Information and Communication Engineering , The University of Tokyo"},{"name":"Kiyoharu Aizawa","slug":"kiyoharu-aizawa","given":"Kiyoharu","family":"Aizawa","affiliation":"Department of Information and Communication Engineering , The University of Tokyo"}],"abstract":"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.\r\nFor these purposes, we present the FolkPopularityRank algorithm, which can score text tags based on their ability to influence the popularity-related numbers. \r\nThe 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.\r\nTo the best of our knowledge, this is the first attempt to recommending tags that can enhance popularity attributes of social media.\r\nWe conducted extensive experiments with about 1,000 images.\r\nWe uploaded the photos with the recommended tags along with the original tags to Flickr as a real test, and obtained very promising results.","doi":"10.24963/ijcai.2017/451","url":"https://ijcai.org/proceedings/2017/451","pdf":"https://ijcai.org/proceedings/2017/0451.pdf","markdown":"https://ijcai.org/proceedings/2017/451.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/451","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":451,"pages":{"first":3231,"last":3237},"keywords":["Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Aydin et al., 2015] Tunc Ozan Aydin, Aljoscha Smolic, and Markus Gross. 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