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  • Recommendation systems have become a common way how to help people when they have to decide in complex selections. Recommender systems have become an integral part of many e-business applications. Various algorithms have been proposed for recommendations and many business solutions are built for different applications. The algorithms are usually based on models of user preferences, preference relations and their learning. Nevertheless, the best approach does not exist here due to the high complexity and uncertainty of the problem. Therefore, a recommendation system typically includes modules with multiple recommender algorithms and the resultant recommendation is created by combination of more partial results. In this paper, we deal with the determination of the grand truth within solving recommendation problem. We use the framework of Dempster-Shafer theory with focus on information fusion. The experiments results show that our proposed method leads to improvements of recommendation over the baseline models.
  • Recommendation systems have become a common way how to help people when they have to decide in complex selections. Recommender systems have become an integral part of many e-business applications. Various algorithms have been proposed for recommendations and many business solutions are built for different applications. The algorithms are usually based on models of user preferences, preference relations and their learning. Nevertheless, the best approach does not exist here due to the high complexity and uncertainty of the problem. Therefore, a recommendation system typically includes modules with multiple recommender algorithms and the resultant recommendation is created by combination of more partial results. In this paper, we deal with the determination of the grand truth within solving recommendation problem. We use the framework of Dempster-Shafer theory with focus on information fusion. The experiments results show that our proposed method leads to improvements of recommendation over the baseline models. (en)
Title
  • A Belief Theoretic Approach to Finding a True Value from Recommendations in E-business
  • A Belief Theoretic Approach to Finding a True Value from Recommendations in E-business (en)
skos:prefLabel
  • A Belief Theoretic Approach to Finding a True Value from Recommendations in E-business
  • A Belief Theoretic Approach to Finding a True Value from Recommendations in E-business (en)
skos:notation
  • RIV/60076658:12510/14:43886775!RIV15-MSM-12510___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • I
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...dnocenehoVysledku
  • 539
http://linked.open...ai/riv/idVysledku
  • RIV/60076658:12510/14:43886775
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  • ground truth; information fusion; user preferences; Dempster-Shafer theory; e-business; Recommendation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [35AD4C6CF046]
http://linked.open...v/mistoKonaniAkce
  • Univerzita Palackého v Olomouci
http://linked.open...i/riv/mistoVydani
  • Olomouc
http://linked.open...i/riv/nazevZdroje
  • MME 2014
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Beránek, Ladislav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Univerzita Palackého v Olomouci
https://schema.org/isbn
  • 978-80-244-4209-9
http://localhost/t...ganizacniJednotka
  • 12510
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