On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Node similarity in the citation graph
Knowledge and Information Systems
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Recommending citations for academic papers
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic classification of citation function
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Towards multi-paper summarization reference information
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A multiple-perspective approach to constructing and aggregating Citation Semantic Link Network
Future Generation Computer Systems
Topic Distributions over Links on Web
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Scholarly paper recommendation via user's recent research interests
Proceedings of the 10th annual joint conference on Digital libraries
Automatically building research reading lists
Proceedings of the fourth ACM conference on Recommender systems
Finding related papers in literature digital libraries
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
Research paper recommender system evaluation: a quantitative literature survey
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
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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.