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

Abstract

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.

Keywords

• rule induction
• second order logic rule systems
• transformation of metadata to values