Communications of the ACM
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A graph-based recommender system for digital library
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Concept-Based Document Recommendations for CiteSeer Authors
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Conceptual recommender system for CiteSeerX
Proceedings of the third ACM conference on Recommender systems
A study of citations in users' online personal collections
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
Research paper recommender system evaluation: a quantitative literature survey
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
Hi-index | 0.01 |
In developing recommendation services for a new digital library called iLumina (www.ilumina-project.org), we are faced with several challenges related to the nature of the data we have available. The availability and consistency of data associated with iLumina is likely to be highly variable. Any recommendation strategy we develop must be able to cope with this fact, while also being robust enough to adapt to additional types of data available over time as the digital library develops. In this paper we describe the challenges we are faced with in developing a system that can provide our users with good, consistent recommendations under changing and uncertain conditions.