Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
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
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Case-Based User Profiling for Content Personalisation
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Visual exploration and incremental utility elicitation
Eighteenth national conference on Artificial intelligence
Improving the Quality of the Personalized Electronic Program Guide
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Effort and accuracy analysis of choice strategies for electronic product catalogs
Proceedings of the 2005 ACM symposium on Applied computing
A hybrid approach to reasoning with partially elicited preference models
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Problem-focused incremental elicitation of multi-attribute tility models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Guest editorial: special issue on a decade of mining the Web
Data Mining and Knowledge Discovery
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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.