Cosine similarity plays a crucial role in measuring the similarity between embeddings, which are numerical representations of data points in a vector space. An embedding transforms complex data, such as words or images, into a format that can easily be understood and processed by machine learning models. Cosine similarity quantifies how similar two embeddings are by measuring the cosine of the angle between them. This approach helps determine how closely related two data points are—ranging from 1 (identical) to -1 (completely dissimilar)—regardless of their magnitude.
For example, in natural language processing (NLP), word embeddings like Word2Vec or GloVe represent words in a high-dimensional space. If you want to find the similarity between the words "king" and "queen," you would compare their embeddings using cosine similarity. Even though the words may have different lengths or scales in their representations, cosine similarity effectively normalizes these factors by focusing solely on the direction of the vectors. This makes it an intuitive choice when working with embeddings in NLP, as the context and meaning of the words can often be captured effectively with this metric.
Furthermore, cosine similarity is not limited to text embeddings. It is also applicable in domains such as image recognition or recommendation systems. For instance, in a collaborative filtering scenario, user and item embeddings can be compared using cosine similarity to recommend items that are most closely related to a user’s preferences. By focusing on the angle between vectors rather than their length, cosine similarity provides a robust method for evaluating similarity across various applications, allowing developers to efficiently match and retrieve relevant data points.