  Expert finding, a popular service provided by many online websites such as
Expertise Finder, LinkedIn, and AMiner, is beneficial to seeking candidate
qualifications, consultants, and collaborators. However, its quality is
suffered from lack of ample sources of expert information. This paper employs
AMiner as the basis with an aim at linking any external experts to the
counterparts on AMiner. As it is infeasible to acquire sufficient linkages from
arbitrary external sources, we explore the problem of zero-shot expert linking.
In this paper, we propose CODE, which first pre-trains an expert linking model
by contrastive learning on AMiner such that it can capture the representation
and matching patterns of experts without supervised signals, then it is
fine-tuned between AMiner and external sources to enhance the models
transferability in an adversarial manner. For evaluation, we first design two
intrinsic tasks, author identification and paper clustering, to validate the
representation and matching capability endowed by contrastive learning. Then
the final external expert linking performance on two genres of external sources
also implies the superiority of the adversarial fine-tuning method.
Additionally, we show the online deployment of CODE, and continuously improve
its online performance via active learning.
