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RULEML
2015
Springer

Transformation and Aggregation Preprocessing for Top-k Recommendation GAP Rules Induction

3 years 8 days ago
Transformation and Aggregation Preprocessing for Top-k Recommendation GAP Rules Induction
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 specific method like "recommend for all users the same set of movies from Spielberg". Our contributions are domain independent data mining methods tailored for top-k which combine second order logic data aggregations and transformations of metadata, especially 5003 open data attributes and general GAP rules mining methods.
Marta Vomlelová, Michal Kopecky, Peter Vojt
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where RULEML
Authors Marta Vomlelová, Michal Kopecky, Peter Vojtás
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