Approximate nearest neighbor (ANN) search is a technique used to quickly find data points in large datasets that are closest to a given query point. Instead of computing the exact nearest neighbors, which can be computationally expensive in high-dimensional spaces, ANN algorithms provide approximate solutions that are faster and more scalable.
In IR, ANN search is commonly applied to vector-based representations of data, such as embeddings from deep learning models. By representing documents or queries as high-dimensional vectors, ANN algorithms like locality-sensitive hashing (LSH), HNSW, or IVFPQ can efficiently find the most relevant documents based on similarity measures like cosine distance or Euclidean distance.
ANN search is particularly useful in semantic search, recommendation systems, and other IR tasks where high-dimensional vectors need to be compared quickly. It enables faster response times, even when dealing with large-scale datasets, making it ideal for real-time applications in industries like e-commerce, healthcare, and social media.