Twitter-Based User Modeling for News Recommendations
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2962-2965.
Abstract
In this paper, we study user modeling on Twitter. We investigate different strategies for mining user interest profiles from microblogging activities ranging from strategies that analyze the semantic meaning of Twitter messages to strategies that adapt to temporal patterns that can be observed in the microblogging behavior. We evaluate the quality of the user modeling methods in the context of a personalized news recommendation system. Our results reveals that an understanding of the semantic meaning of microposts is key for generating high-quality user profiles.
@inproceedings{ijcai2013p437,
title = {Twitter-Based User Modeling for News Recommendations},
author = {Abel, Fabian and Gao, Qi and Houben, Geert-Jan and Tao, Ke},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2962--2965},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/437},
}
TY - CPAPER TI - Twitter-Based User Modeling for News Recommendations AU - Abel, Fabian AU - Gao, Qi AU - Houben, Geert-Jan AU - Tao, Ke T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2962 EP - 2965 UR - https://ijcai.org/proceedings/2013/437 ER -Copied
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