Abstract
Due to the increasing number of conferences, researchers need to spend more and more time browsing through the respective calls for papers (CFPs) to identify those conferences which might be of interest to them. In this paper we study several content-based techniques to filter CFPs retrieved from the web. To this end, we explore how to exploit the information available in a typical CFP: a short introductory text, topics in the scope of the conference, and the names of the people in the program committee. While the introductory text and the topics can be directly used to model the document (e.g. to derive a tf-idf weighted vector), the names of the members of the program committee can be used in several indirect ways. One strategy we pursue in particular is to take into account the papers that these people have recently written. Along similar lines, to find out the research interests of the users, and thus to decide which CFPs to select, we look at the abstracts of the papers that they have recently written. We compare and contrast a number of approaches based on the vector space model and on generative language models.
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References
Bogers, T., van den Bosch, A.: Recommending scientific articles using CiteULike. In: Proc. of the 2008 ACM Conf. on Recommender Systems, pp. 287–290 (2008)
Deng, H., King, I., Lyu, M.R.: Formal models for expert finding on DBLP bibliography data. In: Proc. of the 8th IEEE International Conference on Data Mining, pp. 163–172 (2008)
Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: Proceedings of the 23rd Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 41–48 (2000)
Karimzadehgan, M., Zhai, C., Belford, G.: Multi-aspect expertise matching for review assignment In. In: Proc. of the 17th ACM Conference on Information and Knowledge Management, pp. 1113–1122 (2008)
McNee, S.M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S.K., Rashid, A.M., Konstan, J.A., Riedl, J.: On the recommending of citations for research papers. In: Proc. of the 2002 ACM Conf. on Computer Supported Cooperative Work, pp. 116–125 (2002)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proc. of the 21st Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 275–281 (1998)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24, 513–523 (1988)
Strohman, T., Croft, W.B., Jensen, D.: Recommending citations for academic papers. In: Proc. of the 30th Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 705–706 (2007)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009, 4:2–4:2 (2009)
Tang, W., Tang, J., Tan, C.: Expertise Matching via Constraint-Based Optimization. In: Proc. of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 34–41 (2010)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proc. of the 14th International Conference on Machine Learning, pp. 412–420 (1997)
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Hurtado Martín, G., Schockaert, S., Cornelis, C., Naessens, H. (2013). An Exploratory Study on Content-Based Filtering of Call for Papers. In: Lupu, M., Kanoulas, E., Loizides, F. (eds) Multidisciplinary Information Retrieval. IRFC 2013. Lecture Notes in Computer Science, vol 8201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41057-4_7
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DOI: https://doi.org/10.1007/978-3-642-41057-4_7
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