Vector Similarity Search

Vector similarity search focuses on efficiently finding data points (vectors) most similar to a given query vector within massive datasets. Current research emphasizes optimizing search speed and accuracy, particularly for high-dimensional data, through techniques like asymmetric encoding, novel regularization methods (e.g., weighted vector ranking similarity), and hybrid approaches combining vector similarity with structured data constraints. This field is crucial for numerous applications, including improved performance in machine learning tasks like few-shot learning, financial document classification, and knowledge graph querying, as well as enabling efficient management of large embedding vector databases.