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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ekstrand, M., Kannan, P., Stemper, J., Butler, J., Konstan, J., Riedl, J.: Automatically Building Research Reading Lists. In: Proceedings of the Fourth ACM Conference on Recommender Rystems, pp. 159–166. ACM, New York (2010)
Huang, Z., Qiu, Y.: A Multiple-perspective Approach to Constructing and Aggregating Citation Semantic Link Network. Future Generation Computer Systems 26(3), 400–407 (2010)
Kessler, M.: Bibliographic Coupling between Scientific Papers. American Documentation 14(1), 10–25 (1963)
Lawrence, S., Lee Giles, C., Bollacker, K.: Digital Libraries and Autonomous Citation Indexing. Computer 32(6), 67–71 (1999)
Liang, Y., Li, Q., Qian, T.: Finding Relevant Papers Based on Citation Relations (2011), http://www.cs.cityu.edu.hk/~yicoliang/paper/PaperRec.pdf
Liben-Nowell, D., Kleinberg, J.: The Link-prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology 58(7), 1019–1031 (2007)
Lu, W., Janssen, J., Milios, E., Japkowicz, N., Zhang, Y.: Node Similarity in the Citation Graph. Knowledge and Information Systems 11(1), 105–129 (2007)
McNee, S., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S., Rashid, A., Konstan, J., Riedl, J.: On the Recommending of Citations for Research Papers. In: Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, pp. 116–125. ACM, New York (2002)
Nanba, H., Okumura, M.: Towards Multi-paper Summarization Using Reference Information. In: International Joint Conference on Artificial Intelligence, vol. 16, pp. 926–931 (1999)
Ratprasartporn, N., Ozsoyoglu, G.: Finding Related Papers in Literature Digital Libraries. In: Research and Advanced Technology for Digital Libraries, pp. 271–284 (2007)
Small, H.: Co-citation in the Scientific Literature: A New Measure of the Relationship Between Two Documents. Journal of the American Society for Information Science 24(4), 265–269 (1973)
Strohman, T., Croft, W., Jensen, D.: Recommending Citations for Academic Papers. In: Proceedings of the 30th Annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 706–707 (2007)
Sugiyama, K., Kan, M.: Scholarly Paper Recommendation via User’s Recent Research Interests. In: Proceedings of the 10th annual Joint Conference on Digital Libraries, pp. 29–38. ACM, New York (2010)
Tang, J., Zhang, J., Yu, J., Yang, Z., Cai, K., Ma, R., Zhang, L., Su, Z.: Topic Distributions over Links on Web. In: Proceedings of the 2009 Ninth IEEE International Conference on Data Mining, pp. 1010–1015 (2009)
Teufel, S., Siddharthan, A., Tidhar, D.: Automatic Classification of Citation Function. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 103–110 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liang, Y., Li, Q., Qian, T. (2011). Finding Relevant Papers Based on Citation Relations. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 6897. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23535-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-23535-1_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23534-4
Online ISBN: 978-3-642-23535-1
eBook Packages: Computer ScienceComputer Science (R0)