User Feedback and Preferences Mining

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10125001" target="_blank" >RIV/00216208:11320/12:10125001 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-31454-4_41" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-31454-4_41</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-31454-4_41" target="_blank" >10.1007/978-3-642-31454-4_41</a>

Jazyk výsledku

angličtina

Název v původním jazyce

User Feedback and Preferences Mining

Popis výsledku v původním jazyce

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. Throughoutthe 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.

Název v anglickém jazyce

User Feedback and Preferences Mining

Popis výsledku anglicky

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. Throughoutthe 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP202%2F10%2F0761" target="_blank" >GAP202/10/0761: Semantizace webu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Lecture Notes in Computer Science

ISSN

0302-9743

e-ISSN

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Svazek periodika

neuveden

Číslo periodika v rámci svazku

7379

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

4

Strana od-do

382-386

Kód UT WoS článku

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EID výsledku v databázi Scopus

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