Cosine similarity is a measure used in vector search to determine how similar two vectors are by calculating the cosine of the angle between them. Unlike distance measures like Euclidean, cosine similarity focuses solely on the orientation of vectors in the space, ignoring their magnitude. It is particularly useful in applications like natural language processing (NLP), where semantic similarity between text embeddings matters. Cosine similarity is calculated as the dot product of two vectors divided by the product of their magnitudes.
For example, consider two vectors representing the embeddings of two sentences. If the cosine similarity is close to 1, the sentences are semantically similar. If it is near 0, the sentences are unrelated. Cosine similarity is effective for comparing high-dimensional data like text embeddings because it emphasizes the direction, representing meaning, over the magnitude, which might be influenced by other factors like word frequency.
This metric is often used in search engines, recommendation systems, and clustering algorithms. Its invariance to vector scaling makes it a preferred choice for comparing text documents or normalized datasets. For example, in NLP, comparing word embeddings using cosine similarity helps find synonyms or related concepts efficiently.