BTR-MCL: A Bioinformatics Tool Recommendation Method With Multi-View Contrastive Learning

Digital informatization in bioinformatics research has developed rapidly, resulting in the emergence of tens of thousands of bioinformatics tools in recent decades. However, few researchers systematically study how to manage and recommend bioinformatics tools. Meanwhile, the existing recommendation methods suffer from the incompatibility with bioinformatics tool recommendations, specifically, i) the inability to mine rich semantic information and potential correlations of bioinformatics tools to make up for the sparse problem of operation-tool history interaction; ii) the inability to accommodate recommendations dominated by tools. To address these problems, we collected quantitative bioinformatics operations, tools, and context from https://bio.tools, constructed a knowledge graph dataset for the bioinformatics tools, and proposed the Bioinformatics Tool Recommendation Method with Multi-view Contrastive Learning (BTR-MCL). The method utilizes BERT to enrich the descriptive information and constructs three fine-grained views for multi-view contrastive learning, namely the tool-tool graph, the operation-tool graph, and the bioinformatics tools knowledge graph. By data augmentation and enhancing the importance of tool embedding in local and global contrastive learning, BTR-MCL achieves significant improvements over the state-of-the-art baselines on the top-K recommendation task, outperforming the best baseline by 82.71% on the Recall@10 metric.