GraphRec-based Korean expert recommendation using author contribution index and the paper abstracts in marine

Abstract

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.