@incollection{liang2011finding,
  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.},
  address = {Berlin/Heidelberg},
  affiliation = {Department of Computer Science, City University of Hong Kong, Hong Kong, China},
  author = {Liang, Yicong and Li, Qing and Qian, Tieyun},
  booktitle = {Web-Age Information Management},
  doi = {10.1007/978-3-642-23535-1_35},
  editor = {Wang, Haixun and Li, Shijun and Oyama, Satoshi and Hu, Xiaohua and Qian, Tieyun},
  interhash = {73c03c97c82d13d66f791001dd65688d},
  intrahash = {3c1d75d4210de5cc1f5325598847c046},
  isbn = {978-3-642-23534-4},
  keyword = {Computer Science},
  pages = {403--414},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Finding Relevant Papers Based on Citation Relations},
  url = {http://dx.doi.org/10.1007/978-3-642-23535-1_35},
  volume = 6897,
  year = 2011
}