**YouTube** is an American [online video-sharing](https://en.wikipedia.org/wiki/Video_hosting_service "Video hosting service") [platform](https://en.wikipedia.org/wiki/Web_platform "Web platform") headquartered in [San Bruno, California](https://en.wikipedia.org/wiki/San_Bruno,_California "San Bruno, California")

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A commonly used approach to match similar documents is based on counting the maximum number of common words between the documents.

Still, this approach has an inherent flaw: as the size of the document increases, the number of common words tends to grow even if the documents cover different topics.

The cosine similarity helps overcome this fundamental flaw in the ‘count-the-common-words’ or Euclidean distance approach.

**Input**:

*   TestingDocumentText - string containing the text content to be tested
*   TrainingDocumentText - string containing the text content to be trained

**Output**:

*   CosineSimilarityValue - decimal value ranging between \[0-1\]

Cosine similarity is a metric used to determine how similar the documents are irrespective of their size

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Cosine Similarity