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The Short-term User Modeling for Predictive Applications

  • Michal KompanEmail author
  • Ondrej Kassak
  • Maria Bielikova
Original Article
  • 178 Downloads

Abstract

One of the important purposes of data mining on the web is to reveal hidden characteristics of users including their behavior. These characteristics are often used to analyze previous user actions, his/her preferences, and also to predict the future behavior. An average user session consists of only few actions, which brings several complications for the user modeling and also for subsequent prediction tasks. Such tasks are usually researched from the long-term point of view (e.g., contract renewal or course quit). On the contrary, the short-term user modeling plays an important role in the context of web applications, where it helps to improve user experience. Its shortcoming is that it often requires rich data, which availability is rather rare. For this reason, we propose a novel user model focused on the capturing changes in the user’s behavior on the level of specific actions. The model idea is based on the enrichment of user actions by a comparison of actual user session data with previous sessions. As the model basis on generally available data sources, the approach is applicable to wide scale of existing systems. We evaluate our model by the task of session end intent prediction in the e-learning and news domain. Thanks to reflecting differences in user behavior we are able to predict the intent to end the session for particular user in the scale of his/her next couple of actions. Obtained results clearly show that the proposed model brings higher precision, accuracy and session hit ratio than baseline models.

Keywords

User model Session end intent prediction Web mining Short-term behavior 

Notes

Acknowledgements

This work was partially supported by the Slovak Research and Development Agency under the contract No. APVV-15-0508 grant, the Scientific Grant Agency of the Slovak Republic, Grants No. VG 1/0667/18 and VG 1/0646/15, and is the partial result of the Research&Development Operational Programme for the project ITMS 26240120039 and ITMS 26240220084, co-funded by the European Regional Development Fund.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Slovak University of Technology in BratislavaFaculty of Informatics and Information TechnologiesBratislavaSlovak Republic

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