Identifying key observers to find popular information in advance

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

Research output: Contribution to journalConference articlepeer-review

3 Citations (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.

Original languageEnglish
Pages (from-to)3761-3767
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
Publication statusPublished - 2016
Externally publishedYes
Event25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States
Duration: Jul 9 2016Jul 15 2016

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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