User preferences discovery using fuzzy models

@article{Zenebe2010UserPD,
  title={User preferences discovery using fuzzy models},
  author={A. Zenebe and L. Zhou and A. F. Norcio},
  journal={Fuzzy Sets Syst.},
  year={2010},
  volume={161},
  pages={3044-3063}
}
User preferences discovery aims to learn the patterns of user preferences for various services or items such as movies. Preferences discovery is essential to the development of intelligent personalization applications. Based on decision and utility theories, traditional approaches to preferences discovery explicitly query users about the behavior of value function, or utility of every outcome with respect to each decision criterion. Consequently, these approaches are generally error-prone and… Expand
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41 Citations
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Methods Citations
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41 Citations

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References

SHOWING 1-10 OF 45 REFERENCES
Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems
A statistical model for user preference
Combining Web Usage Mining and Fuzzy Inference for Website Personalization
A maximum entropy web recommendation system: combining collaborative and content features
Getting to know you: learning new user preferences in recommender systems
Similarity of personal preferences: Theoretical foundations and empirical analysis
A movie recommender system based on inductive learning
  • P. Li, S. Yamada
  • Computer Science
  • IEEE Conference on Cybernetics and Intelligent Systems, 2004.
  • 2004
Hybrid Recommender Systems: Survey and Experiments
  • R. Burke
  • Computer Science
  • User Modeling and User-Adapted Interaction
  • 2004
Item-based collaborative filtering recommendation algorithms
E-Commerce Recommendation Applications
...
1
2
3
4
5
...