Comparison of Collaborative and Content-Based Automatic Recommendation Approaches in a Digital Library of Serbian PhD Dissertations

Digital libraries have become an excellent information resource for researchers. However, users of digital libraries would be served better by having the relevant items ‘pushed’ to them. In this research, we present various automatic recommendation systems to be used in a digital library of Serbian PhD Dissertations. We experiment with the use of Latent Semantic Analysis (LSA) in both content and collaborative recommendation approaches, and evaluate the use of different similarity functions. We find that the best results are obtained when using a collaborative approach that utilises LSA and Pearson similarity.

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