About: Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
rdfs:seeAlso
Description
  • In our work, we focus on recommending for small or medium-sized e-commerce portals. Due to high competition, users of these portals lack loyalty and e.g. refuse to register or provide any/enough explicit feedback. Furthermore, products such as tours, cars or furniture have very low average consumption rate preventing us from tracking unregistered user between two consecutive purchases. Recommending on such domains proves to be very challenging, yet interesting research task. We will introduce new method for learning user preferences based on their implicit feedback. The method is based on aggregating various types of implicit feedback with parameterized fuzzy T-norms and S-norms. We have conducted several off-line experiments with real user data from travel agency confirming competitiveness of our method, however further optimizing and on-line experiments should be conducted in the future work.
  • In our work, we focus on recommending for small or medium-sized e-commerce portals. Due to high competition, users of these portals lack loyalty and e.g. refuse to register or provide any/enough explicit feedback. Furthermore, products such as tours, cars or furniture have very low average consumption rate preventing us from tracking unregistered user between two consecutive purchases. Recommending on such domains proves to be very challenging, yet interesting research task. We will introduce new method for learning user preferences based on their implicit feedback. The method is based on aggregating various types of implicit feedback with parameterized fuzzy T-norms and S-norms. We have conducted several off-line experiments with real user data from travel agency confirming competitiveness of our method, however further optimizing and on-line experiments should be conducted in the future work. (en)
Title
  • Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations
  • Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations (en)
skos:prefLabel
  • Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations
  • Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations (en)
skos:notation
  • RIV/00216208:11320/14:10277926!RIV15-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
  • 29938
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/14:10277926
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • T-norms and S-norms, user preference, e-commerce; implicit feedback; recommender systems (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [AF6C39448D1D]
http://linked.open...v/mistoKonaniAkce
  • Munich, Germany
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • E-Commerce and Web Technologies
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
issn
  • 1865-1348
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-319-10491-1_14
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-319-10490-4
http://localhost/t...ganizacniJednotka
  • 11320
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 48 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software