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.

• Type: Journal
• Year: 2015
• Where: RULEML
• Authors: Marta Vomlelová, Michal Kopecky, Peter Vojtás
• Keywords: Internet Technology, RULEML 2015