Manos Tsagkias, Martha Larson, and Maarten de Rijke, submitted a paper at the European Conference on Information Retrieval (ECIR) about predicting podcast preference using easily extracted features from the podcasts feeds. It is based on our previous work on Podcred: A Framework for Analyzing Podcast Preference. The data we used is pulled from Apple iTunes. The paper will be presented at ECIR 2009, held this year in Toulouse, France between 6 and 9 April 2009. The abstract follows:
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.
If you are interested, you can download Exploiting Surface Features for the Prediction of Podcast Preference(pdf), or the presentation slides (pdf).
Update: A proof-of-concept system based on this research is available; its name is podTeller.