Cosine Similarity

What is Cosine Similarity?

Cosine Similarity is a measure of similarity between two non-zero vectors of an inner product space. It is calculated as the cosine of the angle between the vectors and is widely used in text analysis, information retrieval, and machine learning tasks to compare the similarity of documents, words, or other entities represented as vectors.

Example use-cases of Cosine Similarity

  • Document similarity: Comparing the similarity of text documents based on their term frequency-inverse document frequency (TF-IDF) vectors, which represent the importance of words in the documents.

  • Word embeddings: Measuring the semantic similarity between words represented as high-dimensional vectors, such as Word2Vec or GloVe embeddings.

  • Recommender systems: Calculating the similarity between users or items in collaborative filtering algorithms, helping to generate personalized recommendations.

Benefits of Cosine Similarity

  • Invariant to the magnitude of the vectors, making it suitable for comparing entities with different lengths or scales.

  • Efficient to compute, especially for sparse vectors, as it only requires non-zero elements to be considered.

  • Widely used and well-established measure for comparing vectors, supported by many machine learning libraries and tools.

More resources to learn more about Cosine Similarity