Understanding Transparent and Complicated Users as Instances of Preference Learning for Recommender Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10319658" target="_blank" >RIV/00216208:11320/16:10319658 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Understanding Transparent and Complicated Users as Instances of Preference Learning for Recommender Systems

Popis výsledku v původním jazyce

In this paper we are concerned with user understanding in content based recommendation. We assume having explicit ratings with time-stamps from each user. We integrate three different movie data sets, trying to avoid features specific for single data and try to be more generic. We use several metrics which were not used so far in the recommender systems domain. Besides classical rating approximation with RMSE and ratio of order agreement we study new metrics for predicting Next-k and (at least) 1-hit at Next-k. Using these Next-k and 1-hit we try to model display of our recommendation - we can display k objects and hope to achieve at least one hit. We trace performance of our methods and metrics also as a distribution along each single user. We define transparent and complicated users with respect to number of methods which achieved at least one hit. We provide results of experiments with several combinations of methods, data sets and metrics along these three axes.

Název v anglickém jazyce

Understanding Transparent and Complicated Users as Instances of Preference Learning for Recommender Systems

Popis výsledku anglicky

In this paper we are concerned with user understanding in content based recommendation. We assume having explicit ratings with time-stamps from each user. We integrate three different movie data sets, trying to avoid features specific for single data and try to be more generic. We use several metrics which were not used so far in the recommender systems domain. Besides classical rating approximation with RMSE and ratio of order agreement we study new metrics for predicting Next-k and (at least) 1-hit at Next-k. Using these Next-k and 1-hit we try to model display of our recommendation - we can display k objects and hope to achieve at least one hit. We trace performance of our methods and metrics also as a distribution along each single user. We define transparent and complicated users with respect to number of methods which achieved at least one hit. We provide results of experiments with several combinations of methods, data sets and metrics along these three axes.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA15-19877S" target="_blank" >GA15-19877S: Automatické modelování znalostí a plánů pro autonomní roboty</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Mathematical and Engineering Methods in Computer Science

ISBN

978-3-319-29816-0

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

23-34

Název nakladatele

Springer

Místo vydání

Berlín

Místo konání akce

Telč

Datum konání akce

23. 10. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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