![]() ![]() If the angle between vectors (documents) is small, then the cosine of the angle is high, and hence, documents are similar. After the transformation of documents to vectors is done, comparison using cosine similarity is relatively straightforward - we measure the cosine of the angle between their vectors. In that case, documents must be represented as a vector, where a unique word is a dimension and the frequency or weight of that unique word in the document represents the value of that specific dimension. The cosine similarity is often used in text analytics to compare documents and determine if they’re similar and how much. So the cosine similarity formula is equivalent to: The dot product can be expressed as a sum of the product of all vector dimensions and magnitudes as: ![]() Here is the angle between the vectors, AB is the dot product between A and B, while A and B are the magnitudes, or lengths, of vectors A and B, respectively. To compute the cosine similarity between vectors A and B, you can use the following formula: The value -1 means that the vectors are opposite, 0 represents orthogonal vectors, and value 1 signifies similar vectors. Cosine similarity algorithm: Deep diveĬosine similarity is a measure of similarity between two non-zero vectors of an inner product space based on the cosine of the angle between them, resulting in a value between -1 and 1. You’ll also learn how cosine similarity is related to graph databases, exploring the quickest way to utilize it. ![]() After reading this article, you will know precisely what cosine similarity is, how to run it with Python using the scikit-learn library (also known as sklearn), and when to use it. Cosine similarity proved useful in many different areas, such as in machine learning applications, natural language processing, and information retrieval. ![]()
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