Revolutionizing Nearest Neighbor Search with iRangeGraph: Boosting Performance and Reducing Memory Usage in Large-Scale Data Systems
Graph-based methods are playing an increasingly vital role in data retrieval and machine learning , especially in nearest neighbor (NN) searches. NN search is essential for identifying data points closest to a given query, particularly with high-dimensional data like text, images, or audio. With the inefficiency of exact searches in high-dimensional spaces, approximate nearest neighbor (ANN) methods have become crucial, especially graph-based approaches that balance response time and accuracy. These methods are widely used in recommendation engines, e-commerce platforms , and AI-based search systems. One of the main challenges in NN search involves combining vector-based search with additional numeric attribute constraints. For example, a user on an e-commerce platform may want to find products similar to a specific item within a certain price range. Traditional ANN methods either filter out irrelevant data before the search or perform the search without considering cons...