What are the commonly used similarity algorithms in machine learning?

The similarity algorithm is a tool used to measure the similarity between pairs of records, nodes, data points, and texts. These algorithms can calculate similarity based on the distance between two data points, such as using Euclidean distance, or based on text similarity, such as using the Levenshtein algorithm. Similarity algorithms are widely used in many fields, especially in recommendation systems. They can be used to identify similar items or recommend relevant content to users.

Common similarity algorithms

Euclidean distance is a method used to measure the straight-line distance between two points in Euclidean space. Its calculation is simple, so it is widely used in machine learning. However, in cases where the data distribution is uneven, Euclidean distance may not be the best choice.

Cosine similarity: measures the similarity between two vectors based on the angle between them.

Levenshtein algorithm is an algorithm used to measure the distance between two strings. It measures how different two strings are by calculating the minimum number of single-character edits required to convert one string into the other. These editing operations include inserting, deleting, or replacing characters. The Levenshtein algorithm is widely used in spell checking and string matching tasks. By comparing the distance between two strings, we can determine the similarity or difference between them and perform corresponding processing or matching.

Jaro-Winkler algorithm: An algorithm that measures the similarity between two strings based on the number of matching characters and the number of transpositions. It is similar to the Levenshtein algorithm and is commonly used for record linking and entity resolution tasks.

Singular value decomposition (SVD): A matrix decomposition method that decomposes a matrix into the product of three matrices. It is used by the most advanced recommendation systems today.

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