Work

Using hierarchical clustering for learning theontologies used in recommendation systems

Year: 2007

Type: conference-paper

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.

Source: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

DOI: 10.1145/1281192.1281257

Authors: Vincent Schickel-Zuber, Boi Faltings

Institution: École Polytechnique Fédérale de Lausanne

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2164635710

FWCI: 2.429

Cites: 15

Cited by: 37

Related to: 10