Distance metrics in embeddings quantify how similar or different two vector representations are. Embeddings transform data (like words, images, or user behavior) into numerical vectors, and distance metrics provide a way to compare these vectors. Common metrics include Euclidean distance (straight-line distance between points), cosine similarity (angle between vectors), and Manhattan distance (sum of absolute differences). These metrics help determine whether embeddings capture meaningful relationships in the data. For example, in natural language processing (NLP), words with similar meanings should have embeddings closer in vector space, and the right distance metric ensures this.
The choice of distance metric directly impacts how models interpret relationships between embeddings. For tasks like clustering (e.g., grouping similar documents) or retrieval (e.g., finding related products), metrics guide the model’s understanding of proximity. Cosine similarity is often used in text-based embeddings because it focuses on vector direction, making it robust to differences in magnitude (e.g., document length). In contrast, Euclidean distance might better suit scenarios where the absolute position in space matters, like image similarity in a feature space. For instance, a recommendation system using user embeddings might rely on cosine similarity to identify users with similar preferences, even if their activity levels (vector magnitudes) differ.
Developers must consider the data’s characteristics and the task’s requirements when selecting a metric. Sparse or high-dimensional data (e.g., word embeddings) often benefits from cosine similarity, as magnitude differences can be misleading. If embeddings are normalized (scaled to unit length), cosine and Euclidean become interchangeable, but normalization isn’t always practical. Tools like scikit-learn’s NearestNeighbors or FAISS libraries allow specifying metrics during implementation. For example, in an image search application, Euclidean distance might align with pixel-level similarity, while NLP tasks like semantic search might prioritize cosine to focus on semantic alignment. Testing multiple metrics during prototyping can reveal which aligns best with the problem’s goals.