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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F12%3A10125001%21RIV13-GA0-11320___
rdf:type
skos:Concept n14:Vysledek
rdfs:seeAlso
http://link.springer.com/chapter/10.1007%2F978-3-642-31454-4_41
dcterms:description
In this paper, we present our vision and some initial experiments on how to anticipate significance, similarity or polarity of various types of (preferably implicit) user feedback and how to form individual user preference for recommendation. Throughout the corporate web, we can observe the same patterns or actions in user behavior (e.g. page-view, amount of scrolling, rating or purchasing). Recorded user behavior - user feedback - is often used as base for personalized recommendation, but the connection between the feedback and user preference is often unclear or noisy. Our goal is to analyze user behavior in order to understand its relation to the user preference. We report on some initial experiments on a real-world ecommerce application. We describe our new models and methods how to combine various feedback types and how to learn user preferences. In this paper, we present our vision and some initial experiments on how to anticipate significance, similarity or polarity of various types of (preferably implicit) user feedback and how to form individual user preference for recommendation. Throughout the corporate web, we can observe the same patterns or actions in user behavior (e.g. page-view, amount of scrolling, rating or purchasing). Recorded user behavior - user feedback - is often used as base for personalized recommendation, but the connection between the feedback and user preference is often unclear or noisy. Our goal is to analyze user behavior in order to understand its relation to the user preference. We report on some initial experiments on a real-world ecommerce application. We describe our new models and methods how to combine various feedback types and how to learn user preferences.
dcterms:title
User Feedback and Preferences Mining User Feedback and Preferences Mining
skos:prefLabel
User Feedback and Preferences Mining User Feedback and Preferences Mining
skos:notation
RIV/00216208:11320/12:10125001!RIV13-GA0-11320___
n14:predkladatel
n15:orjk%3A11320
n3:aktivita
n11:S n11:P
n3:aktivity
P(GAP202/10/0761), S
n3:cisloPeriodika
7379
n3:dodaniDat
n10:2013
n3:domaciTvurceVysledku
n21:9028773
n3:druhVysledku
n6:J
n3:duvernostUdaju
n5:S
n3:entitaPredkladatele
n12:predkladatel
n3:idSjednocenehoVysledku
176465
n3:idVysledku
RIV/00216208:11320/12:10125001
n3:jazykVysledku
n13:eng
n3:klicovaSlova
recommender systems; implicit feedback; user behavior; User preference
n3:klicoveSlovo
n7:implicit%20feedback n7:User%20preference n7:user%20behavior n7:recommender%20systems
n3:kodStatuVydavatele
DE - Spolková republika Německo
n3:kontrolniKodProRIV
[0883515E731A]
n3:nazevZdroje
Lecture Notes in Computer Science
n3:obor
n16:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n19:GAP202%2F10%2F0761
n3:rokUplatneniVysledku
n10:2012
n3:svazekPeriodika
neuveden
n3:tvurceVysledku
Peška, Ladislav
s:issn
0302-9743
s:numberOfPages
4
n17:doi
10.1007/978-3-642-31454-4_41
n9:organizacniJednotka
11320