Abstract
In this paper, we present our experience in applying collaborative filtering to real-life corporate data. The quality of collaborative filtering recommendations is highly dependent on the quality of the data used to identify users’ preferences. To understand the influence that highly sparse server-side collected data has on the accuracy of collaborative filtering, we ran a series of experiments in which we used publicly available datasets and, on the other hand, a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering.
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Grcar, M., Mladenič, D., Grobelnik, M. (2006). Applying Collaborative Filtering to Real-life Corporate Data. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_45
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DOI: https://doi.org/10.1007/3-540-31314-1_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31313-7
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