Using Rank Aggregation for Expert Search in Academic Digital Libraries
This work addresses the lack of principled approaches for combining evidence in expert finding, which is important for researchers and librarians, but it is incremental as it applies known methods to this specific task.
The paper tackled the problem of expert search in academic digital libraries by exploring unsupervised rank aggregation methods (CombSUM and CombMNZ) to combine multiple estimators of expertise from text, citation graphs, and profiles, with experiments on a Computer Science dataset attesting to their adequacy.
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. This paper explores the usage of unsupervised rank aggregation methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure of the citation patterns for the community of experts, and from profile information about the experts. We specifically experimented two unsupervised rank aggregation approaches well known in the information retrieval literature, namely CombSUM and CombMNZ. Experiments made over a dataset of academic publications for the area of Computer Science attest for the adequacy of these methods.