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Description
  • In this paper, we imagine the situation of a typical e-commerce portal employing personalized recommendation. Such website typically receives user feedback from their implicit behavior such as time on page, scrolling etc. The implicit feedback is generally understood as positive only, however we present several methods how to identify some of the implicit feedback as negative user preference, how to aggregate various feedback types together and how to recommend based on it. We have conducted several off-line experiments with real user data from travel agency website confirming that treating some implicit feedback as negative preference can significantly improve recommendation quality.
  • In this paper, we imagine the situation of a typical e-commerce portal employing personalized recommendation. Such website typically receives user feedback from their implicit behavior such as time on page, scrolling etc. The implicit feedback is generally understood as positive only, however we present several methods how to identify some of the implicit feedback as negative user preference, how to aggregate various feedback types together and how to recommend based on it. We have conducted several off-line experiments with real user data from travel agency website confirming that treating some implicit feedback as negative preference can significantly improve recommendation quality. (en)
Title
  • Negative implicit feedback in e-commerce recommender systems
  • Negative implicit feedback in e-commerce recommender systems (en)
skos:prefLabel
  • Negative implicit feedback in e-commerce recommender systems
  • Negative implicit feedback in e-commerce recommender systems (en)
skos:notation
  • RIV/00216208:11320/13:10192569!RIV14-MSM-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
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
  • 91122
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/13:10192569
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Recommender systems; Negative implicit feedback; Fuzzy tconorms; E-commerce success metrics (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [ECD82D93BABE]
http://linked.open...v/mistoKonaniAkce
  • Madrid, Spain
http://linked.open...i/riv/mistoVydani
  • New York, NY, USA
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Vojtáš, Peter
  • Peška, Ladislav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://bibframe.org/vocab/doi
  • 10.1145/2479787.2479800
http://purl.org/ne...btex#hasPublisher
  • ACM
https://schema.org/isbn
  • 1-4503-1850-9
http://localhost/t...ganizacniJednotka
  • 11320
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