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Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
An architecture for the aggregation and analysis of scholarly usage data
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
A Hybrid, Multi-dimensional Recommender for Journal Articles in a Scientific Digital Library
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
The user-centered design of a recommender system for a universal library catalogue
Proceedings of the sixth ACM conference on Recommender systems
Research paper recommender system evaluation: a quantitative literature survey
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
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This study compares some of the behavioural characteristics of two recommender systems for scholarly articles in a digital library: a usage-based recommender and an experimental citation-based recommender. Experimental results show that article recommendations based only on usage data are slightly better at solving the perennial data-sparsity problem that plagues collaborative filtering recommenders in digital libraries. However, citation-based recommendations are more semantically diverse and have less in common with conventional search results than the usage-based method. However both of these methods are complementary since most of the time if one recommender produces a list of recommendations the other does not.