Learning to Rank for Expert Search in Digital Libraries of Academic Publications
This work addresses the need for more principled approaches in expert finding for academic information retrieval, though it appears incremental as it applies existing learning to rank methods to this domain.
The paper tackled the problem of expert search in digital libraries by proposing a learning to rank method to combine multiple sources of evidence, such as textual content, citation patterns, and profile information, and demonstrated its adequacy through experiments on a Computer Science dataset.
The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence in an optimal way. This paper explores the usage of learning to rank methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure with the citation patterns for the community of experts, and from profile information about the experts. Experiments made over a dataset of academic publications, for the area of Computer Science, attest for the adequacy of the proposed approaches.