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| Research Topic Recommendation for Scholars Based on Heterogeneous Graph Neural Networks |
| Huo Chaoguang1,2, Dan Tingting1, Pang Zengyao1 |
1.School of Information Resource Management, Renmin University of China, Beijing 100872
2.Renmin University of China Libraries, Beijing 100872 |
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Abstract Research topic selection is critical for academic career development. However, identifying suitable candidates from a large number of options remains a significant challenge. To address this issue, this study developed a personalized topic recommendation method to assist scholars discover topics that are relevant to their research bases and interests. Given the limitations of existing recommendation methods in dealing with the heterogeneity of nodes and relationship types in scholarly networks, this study proposes a research topic recommendation model for scholars based on a heterogeneous graph neural network. Three feature aggregation modes for scholars and topics using heterogeneous graph attention network (HAN), heterogeneous graph transformer (HGT), and heterogeneous graph contrastive learning (HGCL) were constructed, with a focus on message passing and aggregation mechanisms to learn the complex interaction patterns between scholars and topics, as well as to capture the patterns of topic diffusion and scholar topic selection. Based on feature aggregation, classifiers such as logistic regression, random forest, and multilayer perceptron have been used for recommendation discrimination. Empirical tests were conducted using data from 6,521 scholars at Renmin University of China and over 30,000 research topics covered by them from the Scopus database. The results showed that the recommendation model based on HGCL integrated with a multilayer perceptron had the highest precision (88.25%), representing a 9.29 percentage points improvement over the baseline model SVD. Meanwhile, the model based on HAN integrated with a multilayer perceptron outperformed the baseline model in terms of recall and F1-score, achieving 96.41% and 91.92%, respectively, representing 17.21 and 12.56 percentage points improvements. This is the first study to construct a research topic recommendation model for scholars, focusing on the representation and learning of heterogeneity in scholar and topic nodes, as well as their relationships. This study provides a reference method for personalized topic selection by scholars and topic-level academic resource recommendations.
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Received: 11 June 2025
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