TY - JOUR
T1 - Identifying key observers to find popular information in advance
AU - Konishi, Takuya
AU - Iwata, Tomoharu
AU - Hayashi, Kohei
AU - Kawarabayashi, Ken Ichi
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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M3 - Conference article
AN - SCOPUS:85006165690
VL - 2016-January
SP - 3761
EP - 3767
JO - IJCAI International Joint Conference on Artificial Intelligence
JF - IJCAI International Joint Conference on Artificial Intelligence
SN - 1045-0823
T2 - 25th International Joint Conference on Artificial Intelligence, IJCAI 2016
Y2 - 9 July 2016 through 15 July 2016
ER -