Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems

Abstract

Ontologies are being successfully used to overcome semanticheterogeneity, and are becoming fundamental elements of the SemanticWeb. Recently, it has also been shown that ontologies can be used tobuild more accurate and more personalized recommendation systems byinferencing missing user's preferences. However, these systemsassume the existence of ontologies, without considering theirconstruction. With product catalogs changing continuously, newtechniques are required in order to build these ontologies in realtime, and autonomously from any expert intervention.This paper focuses on this problem and show that it is possible tolearn ontologies autonomously by using clustering algorithms. Results on the MovieLens and Jester data sets show that recommendersystem with learnt ontologies significantly outperform the classical recommendation approach.