Similarity Measures to Compare Episodes in Modeled Traces

Raafat Zarka; Amélie Cordier; Elod Egyed-Zsigmond; Luc Lamontagne; Alain Mille

Similarity Measures to Compare Episodes in Modeled Traces

Raafat Zarka ¹ Amélie Cordier ¹ Elod Egyed-Zsigmond ² Luc Lamontagne Alain Mille ¹

1 SILEX - Supporting Interaction and Learning by Experience

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

2 DRIM - Distribution, Recherche d'Information et Mobilité

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

Abstract : 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.

Document type :

Conference papers

Domain :

Computer Science [cs]

Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01339182
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Wednesday, June 29, 2016 - 3:48:00 PM
Last modification on : Friday, January 11, 2019 - 5:09:20 PM

Similarity Measures to Compare Episodes in Modeled Traces

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Similarity Measures to Compare Episodes in Modeled Traces

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