Abstract
Exhibition guide system contain various information pertaining to exhibitors, products and events that are happening during the exhibitions. The system would be more useful if it is augmented with a recommender system. Our recommender system would recommend users a list of interesting exhibitors based on associations that mined from the web server logs. The recommendations are ranked based on various Objective Interestingness Measures (OIMs) that quantify the interestingness of an association. Due to data sparsity, some OIMs cannot provide distinct values for different rules and hamper the ranking process. In mobile applications, the ranking of recommendations is crucial because of the low real estate in mobile device screen sizes. We show that our system is able to select an OIM (from 50 OIMs) that would perform better than the regular Support-Confidence OIM. Our system is tested using data from exhibitions held in Germany.
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
Abe, H., Ohsaki, M., Tsumoto, S., Yamaguchi, T.: Evaluating a rule evaluation support method with learning models based on objective rule evaluation indices - A case study with a meningitis data mining result. In: Proceedings of the 5th IEEE International Conference on Hybrid Intelligent Systems, HIS 2005, pp. 169–174. IEEE Computer Society, Washington, DC (2005), http://dx.doi.org/10.1109/ICHIS.2005.37
Belohlavek, R., Grissa, D., Guillaume, S., Nguifo, E.M., Outrata, J.: Boolean factors as a means of clustering of interestingness measures of association rules. In: Proceedings of the 8th International Conference on Concept Lattices and Their Applications (2011)
Bonchi, F., Lucchese, C.: Pushing tougher constraints in frequent pattern mining. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 114–124. Springer, Heidelberg (2005), http://dx.doi.org/10.1007/11430919_15
Bong, K.K.: A Framework for Objective Interestingness Measures Selection. Master’s thesis, The Sirindhorn International Thai German Graduate School of Engineering, King Mongkut’s University of Technology North Bangkok (2012)
Delpisheh, E., Zhang, J.Z.: Evaluating association rules by quantitative pairwise property comparisons. In: Proceedings of the IEEE International Conference on Data Mining Workshops, ICDMW 2010, pp. 927–934. IEEE Computer Society, Washington, DC (2010), http://dx.doi.org/10.1109/ICDMW.2010.145
Delpisheh, E., Zhang, J.Z.: A dynamic composite approach for evaluating association rules. In: The 7th International Conference on Natural Computation (ICNC), pp. 1893–1898 (2011)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Computing Survey 38 (September 2006), http://doi.acm.org/10.1145/1132960.1132963
Grissa, D., Guillaume, S., Nguifo, E.M.: Combining clustering techniques and formal concept analysis to characterize interestingness measures. Computing Research Repository (CoRR) abs/1008.3629 (2010)
Jalali-Heravi, M., Zaïane, O.R.: A study on interestingness measures for associative classifiers. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC 2010, pp. 1039–1046. ACM, New York (2010), http://doi.acm.org/10.1145/1774088.1774306
Lenca, P., Meyer, P., Vaillant, B., Lallich, S.: On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid. European Journal of Operational Research 184(2), 610–626 (2008)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 32–41. ACM, New York (2002), http://doi.acm.org/10.1145/775047.775053
Vaillant, B., Lallich, S., Lenca, P.: Modeling of the counter-examples and association rules interestingness measures behavior. In: Crone, S.F., Lessmann, S., Stahlbock, R. (eds.) The 2nd International Conference on Data Mining (DMIN), pp. 132–137. CSREA Press (2006), http://dblp.uni-trier.de/db/conf/dmin/dmin2006.html#VaillantLL06
Vaillant, B., Lenca, P., Lallich, S.: A clustering of interestingness measures. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 290–297. Springer, Heidelberg (2004)
Wu, J., Zhu, S., Xiong, H., Chen, J., Zhu, J.: Adapting the right measures for pattern discovery: A unified view. The IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics PP(99), 1–12 (2012)
Xianneng, L., Mabu, S., Huiyu, Z., Shimada, K., Hirasawa, K.: Analysis of various interestingness measures in classification rule mining for traffic prediction. In: Proceedings of The Society of Instrument and Control Engineers (SICE) Annual Conference, pp. 1969–1974 (August 2010)
Xuan-Hiep, H., Guillet, F., Briand, H.: Arqat: An exploratory analysis tool for interestingness measures. In: International Symposium on Applied Stochastic Models and Data Analysis (2005)
Zhang, L., Yu, D.L., Wang, Y.G., Zhang, Q.M.: Selecting an appropriate interestingness measure to evaluate the correlation between chinese medicine syndrome elements and symptoms. Chinese Journal of Integrative Medicine, 1–7 (2011), http://dx.doi.org/10.1007/s11655-011-0859-z , doi:10.1007/s11655-011-0859-z
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bong, K.K., Joest, M., Quix, C., Anwar, T. (2014). Automated Interestingness Measure Selection for Exhibition Recommender Systems. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-05476-6_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05475-9
Online ISBN: 978-3-319-05476-6
eBook Packages: Computer ScienceComputer Science (R0)