Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-5392
Bouza, A; Reif, G; Bernstein, A; Gall, H (2008). SemTree: ontology-based decision tree algorithm for recommender systems. In: International Semantic Web Conference, Karlsruhe, Germany, 26 October 2008 - 30 October 2008.
Recommender systems play an important role in supporting people when choosing items from an overwhelming huge number of choices. So far, no recommender system makes use of domain knowledge. We are modeling user preferences with a machine learning approach to recommend people items by predicting the item ratings. Specifically, we propose SemTree, an ontology-based decision tree learner, that uses a reasoner and an ontology to semantically generalize item features to improve the effectiveness of the decision tree built. We show that SemTree outperforms comparable approaches in recommending more accurate recommendations considering domain knowledge.
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|Item Type:||Conference or Workshop Item (Other), refereed, original work|
|Communities & Collections:||03 Faculty of Economics > Department of Informatics|
|Dewey Decimal Classification:||000 Computer science, knowledge & systems|
|Uncontrolled Keywords:||Recommender System, Ontology-Based Decision Tree, User Model, Feature Creation, Semantic Web, Ontology|
|Event End Date:||30 October 2008|
|Deposited On:||21 Nov 2008 09:53|
|Last Modified:||05 Apr 2016 12:33|
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