Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10277926" target="_blank" >RIV/00216208:11320/14:10277926 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-10491-1_14" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-10491-1_14</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-10491-1_14" target="_blank" >10.1007/978-3-319-10491-1_14</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations

Popis výsledku v původním jazyce

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 methodfor 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.

Název v anglickém jazyce

Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations

Popis výsledku anglicky

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 methodfor 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.

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

E-Commerce and Web Technologies

ISBN

978-3-319-10490-4

ISSN

1865-1348

e-ISSN

—

Počet stran výsledku

7

Strana od-do

138-145

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Munich, Germany

Datum konání akce

1. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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