User Behaviour Analysis of a Video-On-Demand Service with a Wide Variety of Subjects and Lengths

  • Authors:

  • M. Vilas;X. G. Paneda;R. Garcia;D. Melendi;V. G. Garcia

  • Affiliations:

  • Department of Computer Science, University of Oviedo, Campus de Viesques Xixón, Spain;Department of Computer Science, University of Oviedo, Campus de Viesques Xixón, Spain;Department of Computer Science, University of Oviedo, Campus de Viesques Xixón, Spain;Department of Computer Science, University of Oviedo, Campus de Viesques Xixón, Spain;Department of Computer Science, University of Oviedo, Campus de Viesques Xixón, Spain

  • Venue:
  • EUROMICRO '05 Proceedings of the 31st EUROMICRO Conference on Software Engineering and Advanced Applications
  • Year:
  • 2005

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Abstract

This paper presents the analysis performed on the www.lne.es video-on-demand service (LNE TV), which is part of the digital version of one of the most important newspapers in Spain. Its principal special characteristic is the wide range of subjects (news, music, culture, tourism, nature, sports, etc) and lengths of the offered contents (from 2 minutes to 2 hours), which make it an interesting case study. Elements about user behaviour have been analyzed such as session analysis, delivered time, pause distribution, jumps length, etc. The study points out interesting results about length dependence, interactions appearing, popularity of the videos, etc, which are compared with the results obtained in previous works, generally developed on educational environments (services or users). A behaviour user model has been performed using the results of the analyses. This model is oriented to services with different types of information and lengths for the videos. This study has been performed thanks to an access log database with more than 150,000 requests of almost 900 videos stored over a period of 4 years. The conclusions of the study are essential to improve the service configuration and content selection. Moreover, they can be used to develop service models for video-on-demand services, which can help administrators to predict future situations and avoid performance problems.