Embedding Based

Embedding-based methods represent data points as vectors in a high-dimensional space, enabling efficient similarity comparisons and downstream tasks like recommendation, knowledge graph completion, and multimodal data retrieval. Current research focuses on improving embedding quality through techniques like contrastive learning with advanced negative sampling strategies, efficient training methods for large datasets, and incorporating contextual information to enhance representation power. These advancements are driving progress in various fields, including healthcare, e-commerce, and natural language processing, by enabling scalable and accurate solutions for complex data analysis problems.