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.

本文提出了 CODE 方法，用于将任意外部专家链接到 AMiner 上，通过对比学习构建专家模型并采用对抗微调方法增强模型的迁移能力，在两个外部数据源上进行评估并展示在线部署和主动学习的性能。

对比预训练与对抗微调结合的零样本专家链接

CODE: Contrastive Pre-training with Adversarial Fine-tuning for  Zero-shot Expert Linking

We study the extent to which online social networks can be connected to open
knowledge bases. The problem is referred to as learning social knowledge
graphs. We propose a multi-modal Bayesian embedding model, GenVector, to learn
latent topics that generate word and network embeddings. GenVector leverages
large-scale unlabeled data with embeddings and represents data of two
modalities---i.e., social network users and knowledge concepts---in a shared
latent topic space. Experiments on three datasets show that the proposed method
clearly outperforms state-of-the-art methods. We then deploy the method on
AMiner, a large-scale online academic search system with a network of
38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our
method significantly decreases the error rate in an online A/B test with live
users.

本研究探讨在线社交网络与开放知识库之间的联系，提出一种基于多模态贝叶斯嵌入模型的算法 GenVector，通过学习潜在话题和生成单词和网络嵌入来将两种不同的数据形式，在共享潜在话题空间中表示。在三个数据集上的实验表明，该算法明显优于现有方法，并在 AMiner 上部署，显著降低了在线 A/B 测试中的错误率。