Exploiting Surface Features for the Prediction of Podcast Preference

  • Manos Tsagkias
  • Martha Larson
  • Maarten de Rijke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5478)


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media, with an application to community-based question answering. In: Web Search and Data Mining (WSDM), pp. 183–194 (2008)Google Scholar
  2. 2.
    Besser, J.: Incorporating User Search Goal Analysis in Podcast Retrieval Optimization. Master’s thesis, Saarland University (2008)Google Scholar
  3. 3.
    de Jong, F.M.G., Westerveld, T., de Vries, A.P.: Multimedia search without visual analysis: The value of linguistic and contextual information. IEEE Transactions on Circuits and Systems for Video Technology 17(3), 365–371 (2007)CrossRefGoogle Scholar
  4. 4.
    Geoghegan, M., Klass, D.: Podcast solutions: The complete guide to podcasting. In: Friends of ED (2005)Google Scholar
  5. 5.
    Kim, S.-M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 423–430 (2006)Google Scholar
  6. 6.
    Liu, Y., Bian, J., Agichtein, E.: Predicting information seeker satisfaction in community question answering. In: SIGIR 2008: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 483–490 (2008)Google Scholar
  7. 7.
    Metzger, M.J., Flanagin, A.J., Eyal, K., Lemus, D.R., McCann, R.: Credibility in the 21st century: Integrating perspectives on source, message, and media credibility in the contemporary media environment, pp. 293–335. Lawrence Erlbaum, Mahwah (2003)Google Scholar
  8. 8.
    Mishne, G.: Applied Text Analytics for Blogs. PhD thesis, University of Amsterdam (2007)Google Scholar
  9. 9.
    Tsagkias, M., Larson, M., Weerkamp, W., de Rijke, M.: Podcred: A framework for analyzing podcast preference. In: Second Workshop on Information Credibility on the Web (WICOW 2008), Napa Valley. ACM, New York (2008)Google Scholar
  10. 10.
    van House, N.: Weblogs: Credibility and collaboration in an online world (2002) (unpublished ms.)Google Scholar
  11. 11.
    Weerkamp, W., de Rijke, M.: Credibility improves topical blog post retrieval. In: HLT-NAACL, pp. 923–931 (2008)Google Scholar
  12. 12.
    Weimer, M., Gurevych, I., Mühlhüser, M.: Automatically assessing the post quality in online discussions on software. In: ACL 2007 Demo and Poster Sessions, pp. 125–128 (2007)Google Scholar
  13. 13.
    Westerveld, T., de Vries, A., Ramírez, G.: Surface features in video retrieval. In: Adaptive Multimedia Retrieval: User, Context, and Feedback, pp. 180–190. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publishers Inc., San Francisco (2005)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Manos Tsagkias
    • 1
  • Martha Larson
    • 2
  • Maarten de Rijke
    • 1
  1. 1.ISLAUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Information and Communication Theory Group, Faculty of EEMCSDelft University of TechnologyThe Netherlands

Personalised recommendations