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Description
  • 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.
  • 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. (en)
Title
  • Learning User Preferences for 2CP-Regression for a Recommender System
  • Learning User Preferences for 2CP-Regression for a Recommender System (en)
skos:prefLabel
  • Learning User Preferences for 2CP-Regression for a Recommender System
  • Learning User Preferences for 2CP-Regression for a Recommender System (en)
skos:notation
  • RIV/67985807:_____/10:00338369!RIV10-AV0-67985807
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET100300517), P(GD201/09/H057), Z(AV0Z10300504)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 268094
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/10:00338369
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • user preferences; machine learning; ordering (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [414BE053E6CF]
http://linked.open...v/mistoKonaniAkce
  • Špindlerův Mlýn
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • SOFSEM 2010. Theory and Practice of Computer Science
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Vojtáš, Peter
  • Eckhardt, Alan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-642-11265-2
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