论文标题
柏油碱：专家推荐系统的多模式代表学习与变压器
BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer
论文作者
论文摘要
专家建议系统的目的是追踪一组候选人的专业知识和偏好，认识他们的专业知识模式并确定专家。在本文中，我们介绍了一种用于专家推荐系统（Berters）的多模式分类方法。在我们提出的系统中，模式来自文本（由候选人发表的文章）和图（其合着者连接）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。同样，一种称为EXEM的图表技术用于从合着者网络中提取候选人的特征。候选人的最终表示是这些向量和其他特征的串联。最终，分类器建立在功能的串联上。这种多模式的方法可以在学术界和社区问题回答中使用。为了验证柏对卧室的有效性，我们分析了其在多标签分类和可视化任务上的性能。
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.
