Embeddings are numerical representations of data that capture the underlying relationships between different items, such as words, sentences, or images. When it comes to similarity comparisons, embeddings transform complex data into a lower-dimensional space, allowing for easier analysis. The key idea is that similar items will have embeddings that are close together in this space, while dissimilar items will be farther apart. This spatial arrangement enables the use of mathematical functions to measure similarity, typically through distance metrics like cosine similarity or Euclidean distance.
In practice, when you want to compare the similarity between two items, you first convert them into their respective embedding vectors using a model. For instance, in natural language processing, words or sentences are transformed into vectors using models like Word2Vec or BERT. Once you have these vectors, you can calculate the distance between them. If two words have similar meanings, their embeddings will result in a small distance value, indicating high similarity. Conversely, if the words are unrelated, their embeddings will be farther apart, resulting in a larger distance.
For example, consider two sentences: "The cat sits on the mat" and "The dog lies on the rug." After embedding, you might find that the vector representations are closer to each other compared to "The cat sits on the mat" and "The computer is on the desk." Such comparisons enable applications like recommendation systems, where knowing how similar items are can help in suggesting relevant content. By leveraging embeddings, developers can implement efficient and meaningful similarity comparisons across various data types, enhancing the functionality of their applications.