Similarity Measures to Compare Episodes in Modeled Traces
- 1k Downloads
This paper reports on a similarity measure to compare episodes in modeled traces. A modeled trace is a structured record of observations captured from users’ interactions with a computer system. An episode is a sub-part of the modeled trace, describing a particular task performed by the user. Our method relies on the definition of a similarity measure for comparing elements of episodes, combined with the implementation of the Smith-Waterman Algorithm for comparison of episodes. This algorithm is both accurate in terms of temporal sequencing and tolerant to noise generally found in the traces that we deal with. Our evaluations show that our approach offers quite satisfactory comparison quality and response time. We illustrate its use in the context of an application for video sequences recommendation.
KeywordsSimilarity Measures Modeled Traces Recommendations Edit Distance Human Computer Interaction
Unable to display preview. Download preview PDF.
- 2.Zarka, R., Champin, P.A., Cordier, A., Egyed-Zsigmond, E., Lamontagne, L., Mille, A.: TStore: A Trace-Base Management System using Finite-State Transducer Approach for Trace Transformation. In: MODELSWARD 2013. SciTePress (2013)Google Scholar
- 3.Rieck, K.: Similarity measures for sequential data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(4), 296–304 (2011)Google Scholar
- 9.Watkins, C.: Dynamic Alignment Kernels. Advances in Large Margin Classifiers, 39–50 (January 1999)Google Scholar
- 10.Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text Classification using String Kernels. Journal of Machine Learning Research 2(3) (2002)Google Scholar
- 11.Cuturi, M., Vert, J.P., Birkenes, O., Matsui, T.: A Kernel for Time Series Based on Global Alignments. In: 2007 IEEE International Conference on Acoustics Speech and Signal Processing, ICASSP 2007, vol. 2(i), pp. II-413–II-416 (2006)Google Scholar
- 16.Montani, S., Leonardi, G.: Retrieval and clustering for supporting business process adjustment and analysis. Information Systems (December 2012)Google Scholar
- 17.Settouti, L.S.: M-Trace-Based Systems - Models and languages for exploiting interaction traces. PhD thesis, University Lyon1 (2011)Google Scholar
- 18.Champin, P.A., Prié, Y., Mille, A.: MUSETTE: a framework for Knowledge from Experience. In: EGC 2004, RNTI-E-2, Cepadues Edition, pp. 129–134 (2004)Google Scholar
- 19.Kietzmann, J.H.: Social media? Get Serious! Understanding the Functional Building Blocks of Social Media 54 (2011)Google Scholar
- 20.Lipkus, A.H.: A proof of the triangle inequality for the Tanimoto distance 26 (1999)Google Scholar