Abstract
Recommendations can help to vanquish the information overload problem on the web. Several web sites provide recommendations for their visitors. If users desire recommendations for sites without this service, they can use browsing agents that give recommendations. In order to obtain user profiles, common agents store interesting and/or uninteresting web pages, use them as training data for the construction of classifiers, and give recommendations for unseen web pages. Feedback via explicit rating is regarded as most reliable but exhausting method to obtain training data. We present three alternative feedback options (explicit, implicit, and hybrid) and evaluate the alternatives via SVMs. We show that feedback options that are less exhausting than explicit rating can be applied successfully.
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© 2009 Springer-Verlag Berlin Heidelberg
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Bomhardt, C., Gaul, W. (2009). Feedback Options for a Personal News Recommendation Tool. In: Gaul, W., Bock, HH., Imaizumi, T., Okada, A. (eds) Cooperation in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00668-5_9
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DOI: https://doi.org/10.1007/978-3-642-00668-5_9
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