User preferences discovery using fuzzy models

@article{Zenebe2010UserPD,
  title={User preferences discovery using fuzzy models},
  author={Azene Zenebe and Lina Zhou and Anthony F. Norcio},
  journal={Fuzzy Sets Syst.},
  year={2010},
  volume={161},
  pages={3044-3063}
}

Figures and Tables from this paper

A Fuzzy Preference Tree-Based Recommender System for Personalized Business-to-Business E-Services
TLDR
Experimental results show that the proposed fuzzy tree-structured user preference profile reflects user preferences effectively and the recommendation approach demonstrates excellent performance for tree- Structured items, especially in e-business applications.
Fuzzy user-interest drift detection based recommender systems
TLDR
A fuzzy user-interest drift detection based recommender system that adapts to user- interest drift and improves prediction accuracy and the results show that the proposed approach improves the performance of recommender systems in metric of MAE.
Application of Trace-Based Subjective Logic to User Preferences Modeling
TLDR
The originality of this explorative research is to associate Subjective Logic (SL) to system’s traces (historical information) in order to model the user preferences that improve the decision process.
Personalized Business to Business E-services using Tree-based Recommender System
TLDR
A method for modelling fuzzy tree-structured user preferences, in which fuzzy set techniques are used to express user preferences is proposed, which is tested and validated using an Australian business dataset and the Movie Lens dataset.
Evaluation of User Model Using Partial Order Relation
TLDR
An evaluation framework on performance of priority in user model is introduced, and the similarity calculation method of interest structure is designed and shows that the proposed method is more successful than traditional method in the measurement of interest priority.
Modeling Personalized Recommendations of Unvisited Tourist Places Using Genetic Algorithms
TLDR
A novel approach based on Genetic Algorithm (GA) to model the interest of user for unvisited location and the recommendation results are comparable with matrix factorization based approach and shows improvement of 4.1 % on average root mean squared error (RMSE).
Modeling Missing Data Based on Neural Fuzzy Inference for Implicit Recommendation
TLDR
This paper proposes Neural Fuzzy Inference based on User preference and Item popularity (UI-NFI) algorithm to model the missing data in implicit recommendation and uses fuzzy set theory to represent user preference and item popularity that get from the history interactions and side information.
Incorporating Content and Context in Recommender Systems
TLDR
To improve recommendation quality in the face of incomplete data, this work proposes several novel approaches for incorporating all available data into collaborative filtering algorithms.
Collaborative Filtering Using Restricted Boltzmann Machine and Fuzzy C-means
TLDR
An attempt is made to cluster the users using FCM clustering algorithm, and then, RBM is used to predict the user’s preferences and the results depict the performance of using both FCM and RBM to build the model for recommendation.
Comprehensive fuzzy tree based recommended System for Search Engine
TLDR
The experimental result on the dataset proves that the proposed methodology using fuzzy tree-structured user preference enables a valid demonstration on tree- Structured items mainly in e-business applications.
...
...

References

SHOWING 1-10 OF 43 REFERENCES
A statistical model for user preference
TLDR
A preference model using mutual information in a statistical framework that combines information of joint features and alleviates problems arising from sparse data is presented.
Combining Web Usage Mining and Fuzzy Inference for Website Personalization
TLDR
This paper presents a fast and intuitive approach to provide Web recommendations using a fuzzy inference engine with rules that are automatically derived from prediscovered user profiles, and achieves high coverage compared to K-NN and nearest-profile recommendations despite slightly lower precision.
A maximum entropy web recommendation system: combining collaborative and content features
TLDR
This work proposes a novel Web recommendation system in which collaborative features such as navigation or rating data as well as the content features accessed by the users are seamlessly integrated under the maximum entropy principle.
Getting to know you: learning new user preferences in recommender systems
TLDR
Six techniques that collaborative filtering recommender systems can use to learn about new users are studied, showing that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.
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
TLDR
The results suggest that inductive-learning-based technology is promising for the solution of the very large-scale problems and high-quality recommendations can be expected.
Hybrid Recommender Systems: Survey and Experiments
  • R. Burke
  • Computer Science
    User Modeling and User-Adapted Interaction
  • 2004
TLDR
This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
Item-based collaborative filtering recommendation algorithms
TLDR
This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
E-Commerce Recommendation Applications
TLDR
An explanation of how recommender systems are related to some traditional database analysis techniques is presented, and a taxonomy ofRecommender systems is created, including the inputs required from the consumers, the additional knowledge required from a database, the ways the recommendations are presented to consumers,The technologies used to create the recommendations, and the level of personalization of the recommendations.
...
...