Paper at ECIR 2009 — Exploiting Surface Features for the Prediction of Podcast Preference

Manos Tsagkias, Martha Larson, and Maarten de Rijke

University of Amsterdam
04 March 2009
Keywords: paper, ecir, speech, information retrieval, predictive analytics

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.

References

[1] Manos Tsagkias, Martha Larson, and Maarten Rijke. 2009. Exploiting Surface Features for the Prediction of Podcast Preference. In Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval (ECIR ‘09). Springer-Verlag, Berlin, Heidelberg, 473–484. ACM Link PDF

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