User Model-Based Information Filtering
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
IEEE Transactions on Knowledge and Data Engineering
Recommenders in a personalized, collaborative digital library environment
Journal of Intelligent Information Systems
Tagging: people-powered metadata for the social web
Tagging: people-powered metadata for the social web
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
Hybrid web recommender systems
The adaptive web
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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Current publication sharing systems inherit from the Web 2.0 philosophy the idea that users can add reusable information to support other peers, enabling them to insert new resources and to tag the existing ones; but, in their current form, these systems suffer of some limitations, such as the lack of tools for supporting users during the creation and organization of their personal concept spaces, and the poor utilization of tags as information sources for producing personalized recommendations. In this paper we propose a model for organizing dynamic and customizable concept spaces, based on innovative structures, and we introduce a mechanism for recommendation, based on tags and mainly on the way in which users connect resources in their concept spaces. Adaptive recommendations are generated analyzing the users' concept spaces, and evaluating the similarities among them in order to reveal the similarity among their goals and perspectives.