Foundations of intelligent tutoring systems
Foundations of intelligent tutoring systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Links: what is an intelligent tutoring system?
intelligence
The Journal of Machine Learning Research
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering
IEEE Intelligent Systems
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical Language Models for Expert Finding in Enterprise Corpora
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Comparing State-of-the-Art Collaborative Filtering Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Toward Social Learning Environments
IEEE Transactions on Learning Technologies
SoNARS: A Social Networks-Based Algorithm for Social Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Recommendations in Online Discussion Forums for E-Learning Systems
IEEE Transactions on Learning Technologies
Advances in Intelligent Tutoring Systems
Advances in Intelligent Tutoring Systems
Motivating participation in social computing applications: a user modeling perspective
User Modeling and User-Adapted Interaction
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In this paper, we present Expediting Expertise, a system designed to provide structured support to the otherwise informal process of social learning in the enterprise. It employs a data-driven approach where online content is automatically analyzed and categorized into relevant topics, topic-specific user expertise is calculated by comparing the models of individual users against those of the experts, and personalized recommendation of learning activities is created accordingly to facilitate expertise development. The system's UI is designed to provide users with ongoing feedback of current expertise, progress, and comparison with others. Learning recommendation is visualized with an interactive treemap which presents estimated return on investment and distance to current expertise for each recommended learning activity. Evaluation of the system showed very positive results.