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