Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems

Ontologies are being successfully used to overcome semantic heterogeneity, and are becoming fundamental elements of the Semantic Web. Recently, it has also been shown that ontologies can be used to build more accurate and more personalized recommendation systems by inferencing missing user's preferences. However, these systems assume the existence of ontologies, without considering their construction. With product catalogs changing continuously, new techniques are required in order to build these ontologies in real time, and autonomously from any expert intervention. This paper focuses on this problem and show that it is possible to learn ontologies autonomously by using clustering algorithms. Results on the MovieLens and Jester data sets show that recommender system with learnt ontologies significantly outperform the classical recommendation approach.


Published in:
KDD '07 Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 599-608
Presented at:
The Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San-Jose, CA, USA, August 12-15, 2007
Year:
2007
ISBN:
978-1-59593-609-7
Keywords:
Laboratories:




 Record created 2007-07-09, last modified 2018-01-28

External link:
Download fulltext
URL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)