An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
IEEE Intelligent Systems
Inferring user's preferences using ontologies
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
OSS: a semantic similarity function based on hierarchical ontologies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Collaborative Filtering Recommendation Algorithm Using Dynamic Similar Neighbor Probability
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Grouping Results of Queries to Ontological Knowledge Bases by Conceptual Clustering
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Clonal selection algorithm for learning concept hierarchy from Malay text
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
How do we measure and improve the quality of a hierarchical ontology?
Journal of Systems and Software
Providing metrics and automatic enhancement for hierarchical taxonomies
Information Processing and Management: an International Journal
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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.