Journal of Computer Science

Interest-based Recommendation in Digital Library

Yan Yang and Jian Zhong Li

DOI : 10.3844/jcssp.2005.40.46

Journal of Computer Science

Volume 1, Issue 1

Pages 40-46

Abstract

With the huge amount and large variety of information available in a digital library, it’s becoming harder and harder for users to identify and get hold of their interested documents. To alleviate the difficulty, personalized recommendation techniques have been developed. Current recommendation techniques rely on similarity between documents. In our work, recommendations are made based on three factors: similarity between documents, information amount, and information novelty. With the introduction of degree of interest, users’ interests can be better characterized. Theoretical analysis and experimental evaluations demonstrate that our techniques can improve both the recommendation recall and recommendation precision.

Copyright

© 2005 Yan Yang and Jian Zhong Li. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.