Abstract
Conceptual models are models used to describe objects or systems in the real world. The quality of a conceptual model heavily depends on the domain knowledge and modeling experience of the individual modeler. Collaborative conceptual modeling is an effective way of building models by taking advantage of collective intelligence. This paper proposes a Co-occurrence Graph based Recommendation Algorithm (CGRA) to implement the collaborative mechanism of conceptual modeling systems. CGRA, inspired by association rule mining algorithm, is an incremental data updating algorithm. The computational complexity of CGRA is much lower than that of the traditional association rule mining based algorithms, while the recommendation effectiveness of these two are almost the same in our collaborative conceptual modeling system, which is revealed by the experiments we have conducted.
K. Fu and S. Wang—These authors contributed equally to this work and should be considered co-first authors.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Record, ACM. 22(2), 207–216 (1993)
Bobadilla, J., Ortega, F., Hernando, A., et al.: Recommender systems survey. J. Knowledge-Based Systems. 46, 109–132 (2013)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Record, ACM. 29(2), 1–12 (2000)
Huang, Z., Chung, W., Chen, H.: A graph model for E-commerce recommender systems. J. Journal of the American Society for Information Science and Technology. 55(3), 259–274 (2004)
Kosters, W.A., Pijls, W., Popova, V.: Complexity analysis of depth first and fp-growth implementations of apriori. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 284–292. Springer, Heidelberg (2003)
Lin, W., Alvarez, S.A., Ruiz, C.: Efficient adaptive-support association rule mining for recommender systems. J. Data Mining and Knowledge Discovery. 6(1), 83–105 (2002)
Zhang, W., Zhao, H., Jiang, Y., et al.: Stigmergy-Based Construction of Internetware Artifacts. J. Software, IEEE. 32(1), 58–66 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fu, K., Wang, S., Zhao, H., Zhang, W. (2015). A Recommendation Algorithm for Collaborative Conceptual Modeling Based on Co-occurrence Graph. In: Liu, L., Aoyama, M. (eds) Requirements Engineering in the Big Data Era. Communications in Computer and Information Science, vol 558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48634-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-48634-4_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-48633-7
Online ISBN: 978-3-662-48634-4
eBook Packages: Computer ScienceComputer Science (R0)