{"type":"ScholarlyArticle","title":"Identifying Key Observers to Find Popular Information in Advance","authors":[{"name":"Takuya Konishi","slug":"takuya-konishi","given":"Takuya","family":"Konishi","affiliation":""},{"name":"Tomoharu Iwata","slug":"tomoharu-iwata","given":"Tomoharu","family":"Iwata","affiliation":""},{"name":"Kohei Hayashi","slug":"kohei-hayashi","given":"Kohei","family":"Hayashi","affiliation":""},{"name":"Ken-ichi Kawarabayashi","slug":"ken-ichi-kawarabayashi","given":"Ken-ichi","family":"Kawarabayashi","affiliation":""}],"abstract":"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.","doi":null,"url":"https://ijcai.org/proceedings/2016/529","pdf":"https://ijcai.org/proceedings/2016/0529.pdf","markdown":"https://ijcai.org/proceedings/2016/529.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/529","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":529,"pages":{"first":3761,"last":3766},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}