Cosine Similarity
Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. In the context of data science, cosine similarity is often used to determine how similar two documents or pieces of text are by comparing the frequency of their words. It is a commonly used metric in information retrieval and text mining. Cosine similarity is also used in recommendation systems to suggest items that are similar to what a user has already shown interest in. It is a useful tool for clustering and classification tasks, as well as for dimensionality reduction techniques such as latent semantic analysis.
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