Transformation and aggregation preprocessing for top-k recommendation GAP rules induction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10318814" target="_blank" >RIV/00216208:11320/15:10318814 - isvavai.cz</a>

Result on the web

<a href="http://ceur-ws.org/Vol-1417/paper18.pdf" target="_blank" >http://ceur-ws.org/Vol-1417/paper18.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Transformation and aggregation preprocessing for top-k recommendation GAP rules induction

Original language description

In this paper we describe the KTIML team approach to RuleML 2015 Rule-based Recommender Systems for the Web of Data Challenge Track. The task is to estimate the top 5 movies for each user separately in a semantically enriched MovieLens 1M dataset. We have three results. Best is a domain specif-ic method like "recommend for all users the same set of movies from Spiel-berg". Our contributions are domain independent data mining methods tailored for top-k which combine second order logic data aggregations and transfor-mations of metadata, especially 5003 open data attributes and general GAP rules mining methods.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/GA15-19877S" target="_blank" >GA15-19877S: Automated Knowledge and Plan Modeling for Autonomous Robots</a><br>

Continuities

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

Publication year

2015

Confidentiality

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

Article name in the collection

Challenge+DC@RuleML 2015

ISBN

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ISSN

1613-0073

e-ISSN

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Number of pages

6

Pages from-to

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Publisher name

CEUR

Place of publication

Berlin

Event location

Berlín

Event date

Aug 2, 2015

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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