ScholarFinder: Knowledge Embedding Based Recommendations Using a Deep Embedded Clustering Model

Bold scientific research tasks need multi-disciplinary knowledge and collaborations that require finding scholars from particular domains with relevant knowledge. Given the variety of scholars and diversity, finding the appropriate scholar is an important and challenging problem for scientific communities. In this paper, we propose a “ScholarFinder” framework that uses contextual information (abstracts or publications) for embedding a scholar's knowledge in an unsupervised learning manner. Specifically, we implement an unsupervised embedding technique viz., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Variational AutoEncoder (VAE)</i>. For better feature representation learning, we also implement a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Variational Deep Embedded Clustering (VDEC)</i> method that further enhances downstream tasks (e.g., clustering, classification) accuracy, scalability, and performance. In addition, we incorporate a multi-task learning scheme into our VDEC model for improving the effectiveness of simultaneously learning both embedding and clustering. Subsequently, the downstream tasks can be built based on pre-trained scholars' knowledge embeddings to predict suitability of a scholar for a research task. Using a dataset involving a 20-year collection of federal grant awards, we have demonstrated how our pre-trained model improved the performance for downstream tasks. We have also investigated how our pre-trained model can be integrated into a knowledge graph to achieve better performance. Lastly, we show that our ScholarFinder model variants outperform state-of-the-art baseline models (i.e., XGBoost, GBDT, AdaBoost, DNN, GraphSAGE, DEC, VaDE) and recent LLM based models (i.e., Bert4Rec, OpenP5) by atleast 18%.