Relevance Prediction

Relevance prediction aims to accurately determine the relationship between a query (e.g., a search term or user request) and a target item (e.g., a document, product, or setting). Current research focuses on improving efficiency and interpretability, exploring model architectures like two-tower models, deep bag-of-words representations, and transformer-based approaches, often incorporating techniques like contrastive learning and data augmentation to enhance performance. These advancements are crucial for applications ranging from e-commerce search and news recommendation to question answering and clinical information retrieval, improving user experience and enabling more effective information access. Addressing challenges like data bias and computational cost remains a key focus.