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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | No |
| Title | SemTree: ontology-based decision tree algorithm for recommender systems |
| Organization Unit | |
| Authors |
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| Presentation Type | other |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | International Semantic Web Conference |
| Event Type | conference |
| Event Location | Karlsruhe, Germany |
| Event Start Date | October 26 - 2008 |
| Event End Date | October 30 - 2008 |
| Abstract Text | 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. |
| Official URL | http://iswc2008.semanticweb.org/ |
| Related URLs | |
| Other Identification Number | merlin-id:275 |
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| Keywords | Recommender System, Ontology-Based Decision Tree, User , , Model, Feature Creation, Semantic Web, Ontology |