The objective of an expert recommendation system is to trace a set of candidates' expertise and preferences, recognize their expertise patterns, and identify experts. In this paper, we introduce a multimodal classification approach for expert recommendation system (BERTERS). In our proposed system, the modalities are derived from text (articles published by candidates) and graph (their co-author connections) information. BERTERS converts text into a vector using the Bidirectional Encoder Representations from Transformer (BERT). Also, a graph Representation technique called ExEm is used to extract the features of candidates from the co-author network. Final representation of a candidate is the concatenation of these vectors and other features. Eventually, a classifier is built on the concatenation of features. This multimodal approach can be used in both the academic community and the community question answering. To verify the effectiveness of BERTERS, we analyze its performance on multi-label classification and visualization tasks.

中文翻译：

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BERTERS：带 Transformer 的专家推荐系统的多模态表示学习

专家推荐系统的目标是追踪一组候选人的专业知识和偏好，识别他们的专业知识模式，并识别专家。在本文中，我们介绍了专家推荐系统（BERTERS）的多模态分类方法。在我们提出的系统中，模态来自文本（候选人发表的文章）和图形（他们的合著者联系）信息。BERTERS 使用来自 Transformer (BERT) 的双向编码器表示将文本转换为向量。此外，一种称为 ExEm 的图表示技术用于从合著者网络中提取候选者的特征。候选者的最终表示是这些向量和其他特征的串联。最终，分类器建立在特征的串联上。这种多模式方法可用于学术界和社区问答。为了验证 BERTERS 的有效性，我们分析了其在多标签分类和可视化任务上的性能。