Exploiting Surface Features for the Prediction of Podcast Preference

  • Authors:

  • Manos Tsagkias;Martha Larson;Maarten Rijke

  • Affiliations:

  • ISLA, University of Amsterdam, Amsterdam, The Netherlands 1098 SJ;Information and Communication Theory Group, Faculty of EEMCS, Delft University of Technology, The Netherlands;ISLA, University of Amsterdam, Amsterdam, The Netherlands 1098 SJ

  • Venue:
  • ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Podcasts display an unevenness characteristic of domains dominated by user generated content, resulting in potentially radical variation of the user preference they enjoy. We report on work that uses easily extractable surface features of podcasts in order to achieve solid performance on two podcast preference prediction tasks: classification of preferred vs. non-preferred podcasts and ranking podcasts by level of preference. We identify features with good discriminative potential by carrying out manual data analysis, resulting in a refinement of the indicators of an existent podcast preference framework. Our preference prediction is useful for topic-independent ranking of podcasts, and can be used to support download suggestion or collection browsing.