Expert recommendation systems recommend specialized experts in a particular field to users based on the knowledge of those experts. However, these systems are limited by the number of experts available and the potential for subjective evaluation, which may result in inappropriate recommendations. Furthermore, we explore the evolution from traditional to deep learning-based recommendation systems, emphasizing graph-based recommendation systems. Nonetheless, deep learning-based systems require large amounts of data, and marine expert recommendation training data are scarce. To address these issues, we constructed and utilized marine expert data in this study. The dataset contains abstracts of marine-related papers and information on their authors. Graphs were generated by assessing the similarity among the abstracts, representing them in a graph format indicative of this similarity, and using the author contribution index to depict the relationship between the abstracts and their respective authors. Various similarity methods and abstract embedding techniques were experimentally explored to realize performance optimization. In the experiments, the optimized model achieved a mean absolute error of 0.7556 and a root-mean-squared error of 1.0421. Notably, this study highlights the limitations of traditional evaluation metrics and proposes the averaged mean reciprocal rank as a suitable alternative. This metric facilitates the quantitative evaluation of model performance on newly created data, obviating a comparison model. Finally, applying the newly constructed data to the GraphRec model by using their graphical representation significantly improves the system performance.

中文翻译：

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基于 GraphRec 的韩国专家推荐，使用海洋领域作者贡献指数和论文摘要

专家推荐系统根据专家的知识向用户推荐特定领域的专业专家。然而，这些系统受到可用专家数量和主观评估潜力的限制，这可能会导致不适当的建议。此外，我们探讨了从传统推荐系统到基于深度学习的推荐系统的演变，重点是基于图的推荐系统。尽管如此，基于深度学习的系统需要大量数据，而海洋专家推荐训练数据却稀缺。为了解决这些问题，我们在本研究中构建并利用了海洋专家数据。该数据集包含海洋相关论文的摘要及其作者信息。通过评估摘要之间的相似性来生成图表，以指示这种相似性的图表格式表示它们，并使用作者贡献指数来描述摘要与其各自作者之间的关系。实验探索了各种相似方法和抽象嵌入技术来实现性能优化。在实验中，优化模型的平均绝对误差为0.7556，均方根误差为1.0421。值得注意的是，这项研究强调了传统评估指标的局限性，并提出平均倒数排名作为合适的替代方案。该指标有助于对新创建的数据的模型性能进行定量评估，从而避免比较模型。最后，通过使用图形表示将新构建的数据应用到 GraphRec 模型中，显着提高了系统性能。