Evaluating the usefulness of citation graph and document metadata in scientific document recommendation for neophytes

Digital libraries have become an essential tool for researchers in all scientific domains. With almost unlimited storage capacities, current digital libraries hold a tremendous number of documents. Though some efforts have been made to facilitate access to documents relevant to a specific information need, for a new researcher who is discovering a research field, such a task remains a real challenge. Indeed neophytes do not necessarily use appropriate keywords to express their information need and they are not necessarily qualified to evaluate correctly the relevance of documents retrieved by the system. In this study, we suppose that the retrieval system in a digital library should take into consideration features other than content-based relevance. To test this hypothesis, we use machine learning methods and build new features from several metadata related to documents. More precisely, we propose to consider as features for machine learning: content-based scores, scores based on the citation graph and scores based on metadata extracted from external resources. As acquiring such features is not a trivial task, we analyze their usefulness and their capacity to detect relevant documents. Our analysis concludes that the use of these additional features improves the performance of the system for a neophyte user. In fact, by adding the new features we find more documents suitable for neophytes within the results returned by the system than when using content-based features alone.