Cosine similarity is a fundamental concept in information retrieval that measures the similarity between documents by evaluating the angle between their vector representations in a multi-dimensional space. Each document is depicted as a vector based on unique terms or words, which allows for the analysis of relationships between ideas. This method, originating from the work of researchers like Gerard Salton, moves beyond simple frequency counts to focus on the distribution of terms, thus providing a more intuitive understanding of document similarity.
Cosine similarity measures the 'angle' between document vectors, providing a nuanced approach to understanding the relationship between ideas and concepts.
By representing documents as multi-dimensional vectors, cosine similarity offers insights into the relative distribution of terms and the underlying concepts they convey.
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