Identifying key observers to find popular information in advance

Takuya Konishi, Tomoharu Iwata, Kohei Hayashi, Ken Ichi Kawarabayashi

研究成果: ジャーナルへの寄稿会議記事査読

3 被引用数 (Scopus)


Identifying soon-to-be-popular items in web services offers important benefits. We attempt to identify users who can find prospective popular items. Such visionary users are called observers. By adding observers to a favorite user list, they act to find popular items in advance. To identify efficient observers, we propose a feature selection based framework. This uses a classifier to predict item popularity, where the input features are a set of users who adopted an item before others. By training the classifier with sparse and non-negative constraints, observers are extracted as users whose parameters take a non-zero value. In experiments, we test our approach using real social bookmark datasets. The results demonstrate that our approach can find popular items in advance more effectively than baseline methods.

ジャーナルIJCAI International Joint Conference on Artificial Intelligence
出版ステータス出版済み - 2016
イベント25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, 米国
継続期間: 7月 9 20167月 15 2016

!!!All Science Journal Classification (ASJC) codes

  • 人工知能


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