A Study on the Granularity of User Modeling for Tag Prediction
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A Machine Learning Based Framework for Adaptive Mobile Learning
ICWL '009 Proceedings of the 8th International Conference on Advances in Web Based Learning
An efficient approach for building customer profiles from business data
Expert Systems with Applications: An International Journal
Experiments on the preference-based organization interface in recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive algorithm based on clustering techniques for custom reading plans
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
An adaptive videos enrichment system based on decision trees for people with sensory disabilities
Proceedings of the International Cross-Disciplinary Conference on Web Accessibility
Prediction of socioeconomic levels using cell phone records
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
AHSPeR: adaptive hypermedia system oriented toward personalization of readings plans
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
Artificial Intelligence Review
Proceedings of the 17th Panhellenic Conference on Informatics
| Hi-index | 0.00 |
The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the application