cosine similarity

Terms from Artificial Intelligence: humans at the heart of algorithms

Page numbers are for draft copy at present; they will be replaced with correct numbers when final book is formatted. Chapter numbers are correct and will not change now.

The cosine similarity measures how close two vectors are to one another using the cosine of the angle between them. Unlike a Euclidean distance, the cosine similarity ignores the size of the two vectors, it just focuses on their direction. In {{document retrieval}] and text processing, the cosine silarity is applied to the vector representing the frequencies of words in a document, soemtimes weighted by overalll corpus frequency.

Used in Chap. 10: pages 152, 153