From Text to Recommendations: How Vector Databases are Revolutionizing Personalized Content Delivery

Abstract: Effective similarity search and retrieval are now possible thanks to vector databases, which have become a potent tool for organizing and searching high-dimensional data. This article provides a thorough examination of vector databases, their underlying theories, and their applications across a range of industries. In handling complex data types, we address the significance of vector representation and emphasize the benefits of vector databases over traditional databases 1. The article explores the process of creating a vector database, highlighting the critical function of indexing strategies such as IVF (Inverted File Indexing) and HNSW (Hierarchical Navigable Small World) in guaranteeing the effectiveness and precision of searches 2. In addition, we discuss the problems caused by the curse of dimensionality and offer solutions to lessen its effects on nearest neighbor searches 3