Similarity search is crucial for various applications such as recommendation systems, information retrieval, and clustering algorithms. It involves algorithms and data structures designed to quickly retrieve data points that are nearest to a specified query point in a dataset. The effectiveness of a similarity search depends on the metric used to define "distance" between data points, which can be Euclidean distance, cosine similarity, Manhattan distance, or others, depending on the context. Advanced techniques like approximate nearest neighbor (ANN) algorithms have been developed to perform similarity searches efficiently on large, high-dimensional data sets, balancing precision with computational efficiency.
Historical overview: The concept of similarity search has been fundamental in computer science and information retrieval for several decades, with significant development noted since the 1960s. The term gained more prominence in the 1990s as digital data volumes grew exponentially, necessitating more efficient algorithms.
Key contributors: While many researchers have contributed to the field of similarity search, pivotal developments include the introduction of spatial data structures like KD-trees by Jon Bentley in 1975 and the later advancement of locality-sensitive hashing (LSH) in the late 1990s. These technologies laid foundational techniques widely used in the field today.