Cosinuslikhet

Cosinuslikhet, also known as cosine similarity, is a measure of similarity between two non-zero vectors in an inner product space. It is a scalar value that indicates the cosine of the angle between the two vectors. The value of cosine similarity ranges from -1 to 1. A value of 1 means the vectors are identical in direction, 0 means they are orthogonal (independent), and -1 means they are diametrically opposed.

The formula for cosine similarity between two vectors A and B is calculated as the dot product of A and B divided by the product of their magnitudes: cos(θ) = (A · B) / (||A|| ||B||). This formula is particularly useful in fields like information retrieval and text analysis, where documents or queries can be represented as vectors. In these contexts, cosine similarity helps determine how semantically similar two pieces of text are.

For instance, if two documents contain similar keywords and phrases, their vector representations will point in similar directions, resulting in a high cosine similarity score. Conversely, documents with very different content will have vectors that are nearly orthogonal, yielding a score close to zero. Its invariance to the magnitude of the vectors makes it a suitable metric when the length of the documents or vectors is not a primary concern for determining similarity.