Cosine Similarity
Cosine similarity is a metric used to measure how similar two vectors are by calculating the cosine of the angle between them in a multi-dimensional space. It ranges from -1 (completely opposite) to 1 (identical), with 0 indicating orthogonality or no similarity. This measure is widely applied in fields like natural language processing, information retrieval, and recommendation systems to compare documents, text embeddings, or user preferences.
Developers should learn cosine similarity when working on tasks involving similarity measurement, such as text analysis, clustering, or building recommendation engines. It is particularly useful for handling high-dimensional data where Euclidean distance might be less effective due to the curse of dimensionality, and it is computationally efficient for sparse vectors, making it ideal for applications like document similarity in search algorithms or collaborative filtering in e-commerce platforms.