Interpreting Web Shop User's Behavioral Patterns as Fictitious Explicit Rating for Preference Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10277916" target="_blank" >RIV/00216208:11320/14:10277916 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-09870-8_19" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-09870-8_19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-09870-8_19" target="_blank" >10.1007/978-3-319-09870-8_19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interpreting Web Shop User's Behavioral Patterns as Fictitious Explicit Rating for Preference Learning

Popis výsledku v původním jazyce

We consider applications of user preference rule learning in marketing. We chose rules because of human-understandability. We chose fuzzy logic because it enables to order items for recommendation. In this paper we introduce a rule based system equivalent to the Fagin-Lotem-Naor preference system. We show a multi-user version, introduce induction and compare it to several methods for learning user preference. The methods are based, first, on interpreting e-shop user's behavioral patterns collected by scripts as fictitious explicit rating. After this we use this (fictitious) explicit rating for content based preference learning. Our main motivation is on recommending for small or medium-sized e-commerce portals. Due to high competition, users of these portals are not too loyal 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 betw

Název v anglickém jazyce

Interpreting Web Shop User's Behavioral Patterns as Fictitious Explicit Rating for Preference Learning

Popis výsledku anglicky

We consider applications of user preference rule learning in marketing. We chose rules because of human-understandability. We chose fuzzy logic because it enables to order items for recommendation. In this paper we introduce a rule based system equivalent to the Fagin-Lotem-Naor preference system. We show a multi-user version, introduce induction and compare it to several methods for learning user preference. The methods are based, first, on interpreting e-shop user's behavioral patterns collected by scripts as fictitious explicit rating. After this we use this (fictitious) explicit rating for content based preference learning. Our main motivation is on recommending for small or medium-sized e-commerce portals. Due to high competition, users of these portals are not too loyal 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 betw

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Rules on the Web. From Theory to Applications

ISBN

978-3-319-09869-2

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

15

Strana od-do

251-265

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Prague, Czech Republic

Datum konání akce

18. 8. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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