The three semantics of fuzzy sets
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Similarity and compatibility in fuzzy set theory: assessment and applications
Similarity and compatibility in fuzzy set theory: assessment and applications
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Similarity of personal preferences: theoretical foundations and empirical analysis
Artificial Intelligence
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
A Statistical Model for User Preference
IEEE Transactions on Knowledge and Data Engineering
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
An approach for combining content-based and collaborative filters
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
| Hi-index | 0.20 |
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 labor intensive. Although implicit elicitation approaches have been proposed to address the above limitations, extent approaches largely ignore multi-valued nature of item features and uncertainty associated with item features and user preferences. To address uncertainty due to vagueness and imprecision, this research proposed a general framework for preferences discovery based on fuzzy set theories. In addition, new fuzzy models were created for preferences discovery and representation. Further, an algorithm was developed to predict user preferences with uncertainty, and visualization of item features, user feedback, and the discovered preferences helped improve the interpretation of the discovered knowledge. The results of the simulation evaluation using a benchmark movie dataset revealed that the proposed preference discovery method: (1) doubled the accuracy of preference discovery as compared to random prediction; and (2) outperformed conventional techniques in making movie recommendation. These findings suggest that fuzzy models are effective for preferences patterns discovery, and personalized recommendation application.