User modeling in intelligent information retrieval
Information Processing and Management: an International Journal - Artificial Intelligence and Information Retrieval
C4.5: programs for machine learning
C4.5: programs for machine learning
Agents that reduce work and information overload
Communications of the ACM
A shell for developing non-monotonic user modeling systems
International Journal of Human-Computer Studies
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Acquiring User Preferences for Information Filtering in Interactive Multi-Media Services
PRICAI '96 Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Machine learning of user profiles: representational issues
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
ITERATE: a conceptual clustering algorithm for data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
| Hi-index | 0.00 |
In this paper we propose a methodology for organising the users of an information providing system into groups with common interests (communities). The communities are built using unsupervised learning techniques on data collected from the users (user models). We examine a system that filters news on the Internet, according to the interests of the registered users. Each user model contains the user's interests on the news categories covered by the information providing system. Two learning algorithms are evaluated: COBWEB and ITERATE. Our main concern is whether meaningful communities can be constructed. We specify a metric to decide which news categories are representative for each community. The construction of meaningful communities can be used for improving the structure of the information providing system as well as for suggesting extensions to individual user models. Encouraging results on a large data-set lead us to consider this work as a first step towards a method that can easily be integrated in a variety of information systems.