# Using Rank Aggregation for Expert Search in Academic Digital Libraries

arXiv: https://arxiv.org/abs/1501.05140 · HTML: https://mlsanity.com/paper/1501.05140/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2015.

## Built on by (most cited first, 2 of 2)

- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study](https://mlsanity.com/paper/2302.02940/) (2023)

## Abstract (arXiv)

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.