Real Use Cases for Cosine Similarity, Dot Product, and Euclidean Distance

Vector similarity measures are explained through real-world analogies to clarify their distinct use cases. Cosine similarity focuses on the direction of vectors, ignoring their magnitude, making it ideal for comparing text documents of different lengths like resumes and job descriptions. The dot product considers both direction and magnitude, which is useful for recommendation systems where intensity or popularity is a factor. Euclidean distance measures the straight-line difference between points, making it suitable for clustering applications like customer segmentation or identifying similar patient profiles based on health metrics.