Cosine similarity is a measure used to determine how similar two vectors are, regardless of their magnitude. It’s widely used in various fields, such as text analysis, data mining, and machine learning. Here’s a brief overview: Cosine Similarity Formula The cosine similarity between two vectors (A) and (B) is calculated as: \(\text{cosine similarity} = \frac{A \cdot B}{|A| |B|}\) Where: (A \cdot B) is the dot product of the vectors. (|A|) and (|B|) are the magnitudes (or lengths) of the vectors. Applications of Cosine Similarity Text Analysis: Used to measure the similarity between documents by representing them as vectors of word frequencies. Recommendation Systems: Helps in finding similar items or users based on their preferences. Clustering: Assists in grouping similar data points together in clustering algorithms.