A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Using Decision Trees for Agent Modeling: Improving Prediction Performance
User Modeling and User-Adapted Interaction
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Collaborative Filtering Using Weighted Majority Prediction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Understandable Learner Models for a Sensorimotor Control Task
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
A Statistical Model for User Preference
IEEE Transactions on Knowledge and Data Engineering
Fast webpage classification using URL features
Proceedings of the 14th ACM international conference on Information and knowledge management
Introduction to the special issue on statistical and probabilistic methods for user modeling
User Modeling and User-Adapted Interaction
Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Identifying useful features for classification and forecast tasks from a ranked data is highly difficult and challenging. By ranking user popularity ratings from normalised area histograms, a method of feature selection for ranked data inspired from the law of vital few is proposed. We propose that the attributes that are most stable against the variations in classes have their usefulness in a forecasting task, while the attributes that are most unstable between inter-class samples but most stable within intra-class samples have their usefulness in classification tasks. The performance of the proposed method is demonstrated through a realistic example of web-content data from Yahoo! research repository: the user rating of web pages. The attributes in the data when ranked based on their importance in a year show distinct characteristics of performance in the tasks of popularity forecast and classification.