Semantic and explainable research-related recommendation system based on semi-supervised methodology using BERT and LDA models

With the launch of academic search engines such as Google Scholar, Microsoft Academic, and Scopus, researchers are using them to access scholarly materials on a large scale without making any payment. In this optimal research environment, academic libraries or community databases have naturally experienced a rapid increase in volume; however, the excessive quantitative growth of information, or information overload, acts as a double-edged sword that prevents researchers from finding relevant prior studies or researchers with similar interests. Existing keyword and rule-based search systems carry the risk of recommending research literature with a high citation frequency rather than recommending contextually similar documents. Therefore, this study proposes a semi-supervised semantic-based research literature and researcher recommendation system using LDA and BERT. Since a semi-supervised method is used, research literature can be embedded based on contextual and classification information, and the global topic information of the research literature can also be captured. In addition, a research literature information extractor system has been implemented, which comprises classification network of our model and an explainable keywords extractor system implemented using BERT's self-attention structure. Based on the experimental results, it can be confirmed that the proposed study model shows better performance than other baselines. This model can help users to find information and contextual matching data quickly and accurately from the database. In addition, when implementing an academic library and community database, such a powerful research-related recommendation system and research literature information extractor are expected to have an enormous effect.