Liang, Y.; Li, Q. & Qian, T.: Finding Relevant Papers Based on Citation Relations. In: Wang, H.; Li, S.; Oyama, S.; Hu, X. & Qian, T. (Hrsg.): Web-Age Information Management. Berlin/Heidelberg: Springer, 2011 (Lecture Notes in Computer Science 6897), S. 403-414
[Volltext]
With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques.
@incollection{liang2011finding,
author = {Liang, Yicong and Li, Qing and Qian, Tieyun},
title = {Finding Relevant Papers Based on Citation Relations},
editor = {Wang, Haixun and Li, Shijun and Oyama, Satoshi and Hu, Xiaohua and Qian, Tieyun},
booktitle = {Web-Age Information Management},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
address = {Berlin/Heidelberg},
year = {2011},
volume = {6897},
pages = {403--414},
url = {http://dx.doi.org/10.1007/978-3-642-23535-1_35},
doi = {10.1007/978-3-642-23535-1_35},
isbn = {978-3-642-23534-4},
keywords = {item, recommender, citation},
abstract = {With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques.}
}
Li, Q.: Cyberbullying in Schools: A research of gender differences. In: School Psychology International (2006), Nr. 2, S. 157-170
@article{li2006cyberbullying,
author = {Li, Qing},
title = {Cyberbullying in Schools: A research of gender differences},
journal = {School Psychology International},
year = {2006},
number = {2},
pages = {157-170},
keywords = {Cybermobbing_Gender}
}
Li, Q.: New bottle but old wine: A research of cyberbullying in schools. In: Computers in human behavior (2005), Nr. 4, S. 1177-1791
@article{li2005bottle,
author = {Li, Qing},
title = {New bottle but old wine: A research of cyberbullying in schools},
journal = {Computers in human behavior},
year = {2005},
number = {4},
pages = {1177-1791},
keywords = {Cybermobbing}
}