Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction models. We propose a new method that extends the utility model and assumes that the structure of user preferences follows an ontology of product attributes. Using the data of the MovieLens system, we show experimentally that real user preferences indeed closely follow an ontology based on movie attributes. Furthermore, a recommender based just on a single individual’s preferences and this ontology performs better than collaborative filtering, with the greatest differences when little data about the user is available. This points the way to how proper inductive bias can be used for significantly more powerful recommender systems in the future.


Editor(s):
Nasraoui, Olfa
Zaïane, Osmar
Spiliopoulou, Myra
Mobasher, Bamshad
Masand, Brij
Yu, Philip S.
Published in:
Advances in Web Mining and Web Usage Analysis, 39-57
Presented at:
7th International Workshop on Knowledge Discovery on the Web, WebKDD 2005, Chicago, IL, USA, August 21, 2005
Year:
2006
Publisher:
Berlin, Heidelberg, Springer
Other identifiers:
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 Record created 2006-05-19, last modified 2018-01-27

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