Abstract
Graduate students, professors and researchers regularly access, review, and use large amounts of literature. In previous work, we presented Papyres, a Research Paper Management Systems, which combines bibliography functionalities along with paper recommender techniques and document management tools, in order to provide a set of functionalities to locate research papers, handle and maintain the bibliographies, and to manage and share knowledge about the research literature. In this work we detail Papyres’ paper recommendation technique. Specifically, Papyres employs a Hybrid recommender system that combines both Content-based and Collaborative filtering to help researchers locate research material. Particularly, in this work special attention is given to the Collaborative filtering process, were a multi-criteria approach is used to evaluate the articles, allowing researchers to denote their interest in specific parts of articles. Moreover, we propose, test and compare several approaches to determine the neighbourhood in the Collaborative filtering process such as to increase the accuracy of the recommendation.
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
Brusilovsky, P., Farzan, R., Jae-wook, A.: Comprehensive personalized information access in an educational digital library. In: 5th ACM/IEEE-CS Joint Conference on Digital Libraries, Denver, CO, USA, pp. 9–18 (2005)
Kapoor, N., Chen, J., Butler, J.T., Fouty, G.C., Stemper, J.A., Riedl, J., Konstan, J.A.: Techlens: a researcher’s desktop. In: ACM Conference on Recommender Systems (RecSys 2007), Minneapolis, MN, USA, pp. 183–184 (2007)
Torres, R., McNee, S., Abel, M., Konstan, J., Riedl, J.: Enhancing digital libraries with TechLens+. In: 4th ACM/IEEE-CS joint conference on Digital libraries (JCDL 2004), Tuscon, USA, pp. 228–236 (2004)
Huang, X., An, A., Cercone, N., Promhouse, G.: Discovery of Interesting Association Rules from Livelink Web Log Data. In: IEEE International Conference on Data Mining (ICDM 2002), Maebashi, Japan, pp. 763–766 (2002)
Naak, A., Hage, H., Aïmeur, E.: Papyres: a Research Paper Management System. In: IEEE Joint Conference on E-Commerce Technology (CEC 2008) and Enterprise Computing, E-Commerce and E-Services (EEE 2008), Crystal City, Washington (2008)
O’Reilly, T.: What Is Web 2.0 (2005) (accessed, January 2008), http://www.oreillynet.com/
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)
Lakiotaki, K., Tsafarakis, S., Matsatsinis, N.: UTA-Rec: a recommender system based on multiple criteria analysis. In: ACM Conference on Recommender Systems (RecSys 2008), Lausanne, Switzerland, pp. 219–226 (2008)
Matsatsinis, N.F., Kleanthi, L., Pavlos, D.: A System based on Multiple Criteria Analysis for Scientific Paper Recommendation. In: 11th Panhellenic Conference in Informatics, Patras, Greece, pp. 135–149 (2007)
Vassileva, J.: Harnessing P2P Power in the Classroom. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 305–314. Springer, Heidelberg (2004)
Tang, T.Y.: The design and study of pedagogical paper recommendation. Doctoral Thesis. University of Saskatchewan, Saskatoon (2008)
Tang, T.Y., McCalla, G.I.: A multi-dimensional paper recommender. In: 13th International Conference on Artificial Intelligence in Education Marina Del Rey, Los Angeles, California, U.S.A (2007)
Mao, Y., Vassileva, J., Grassmann, W.: A System Dynamics Approach to Study Virtual Communities. In: 40th Annual Hawaii International Conference on System Sciences, Waikoloa, Hawaii, p. 178a (2007)
Bollacker, K., Lawrence, S., Giles, L.: A System for Automatic Personalized Tracking of Scientific Literature on the Web. In: 4th ACM Conference on Digital Libraries, Berkeley, California, United States, pp. 105–113 (1999)
Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)
IEEE Standard for Learning Object Metadata. IEEE Std 1484.12.1-2002, i-32 (2002)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for Improved recommendations. In: 18th National Conference on Artificial Intelligence, Edmonton, Alberta, Canada, pp. 187–192 (2002)
Adomavicius, G., Kwon, Y.: New Recommendation Techniques for Multicriteria Rating Systems. IEEE Intelligent Systems 22(3), 48–55 (2007)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Naak, A., Hage, H., Aïmeur, E. (2009). A Multi-criteria Collaborative Filtering Approach for Research Paper Recommendation in Papyres. In: Babin, G., Kropf, P., Weiss, M. (eds) E-Technologies: Innovation in an Open World. MCETECH 2009. Lecture Notes in Business Information Processing, vol 26. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01187-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-01187-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01186-3
Online ISBN: 978-3-642-01187-0
eBook Packages: Computer ScienceComputer Science (R0)