Abstract
In this paper we deal with a task to learn a general user model from user ratings of a small set of objects. This general model is used to recommend top-k objects to the user. We consider several (also some new) alternatives of learning local preferences and several alternatives of aggregation (with or without 2CP-regression). The main contributions are evaluation of experiments on our prototype tool PrefWork with respect to several satisfaction measures and the proposal of method Peak for normalisation of numerical attributes. Our main objective is to keep the number of sample data which the user has to rate reasonable small.
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Eckhardt, A., Vojtáš, P. (2010). Learning User Preferences for 2CP-Regression for a Recommender System. In: van Leeuwen, J., Muscholl, A., Peleg, D., Pokorný, J., Rumpe, B. (eds) SOFSEM 2010: Theory and Practice of Computer Science. SOFSEM 2010. Lecture Notes in Computer Science, vol 5901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11266-9_29
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DOI: https://doi.org/10.1007/978-3-642-11266-9_29
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