Our current work on a ontology-based decision tree algorithm to learn user preferences was accepted and published at 7th International Semantic Web Conference (ISWC) 2008 in Karlsruhe (Germany). The paper is available online at CEUR-WS.org as volume 401.
It was my first participation of a conference where I had my first opportunity to present our early work on a novel approach on inducing a decision tree based on a domain ontology. SemTree is a machine learning algorithm based on the concept of decision tree induction. The novelty about SemTree is the usage of a domain ontology to learn more accurate models. That has been shown in a preliminary evaluation on a movie dataset.
At the poster session I had great conversations and discussions about recommender systems and my approach. But the disadvantage of presenting a poster to the research community is that oneself cannot take a look after the other interesting posters and demos.
To my surprise, Karlsruhe is a very nice german city dominated by students – a typical students town. That shows the high frequency of small bars and restaurants. The castle looks pretty and the very large castle park is absolutely beautiful. I would have liked to enjoy this park in the summer and spend my leisure time with friends there.
Abstract
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.
Presentation
In the following, you can see a preview of the SemTree poster titled “seMANtics IN TREEs” in allusion to the american TV series “Man in Trees”:
Downloads
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