Identifying Key Observers to Find Popular Information in Advance
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3761-3766.
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.
@inproceedings{ijcai2016p529,
title = {Identifying Key Observers to Find Popular Information in Advance},
author = {Konishi, Takuya and Iwata, Tomoharu and Hayashi, Kohei and Kawarabayashi, Ken-ichi},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3761--3766},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/529},
}
TY - CPAPER TI - Identifying Key Observers to Find Popular Information in Advance AU - Konishi, Takuya AU - Iwata, Tomoharu AU - Hayashi, Kohei AU - Kawarabayashi, Ken-ichi T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3761 EP - 3766 UR - https://ijcai.org/proceedings/2016/529 ER -Copied
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