Distance Type

This page provides basic information about values in the Distance Type field, which is used in defining semantic search views in Macrometa Global Data Network (GDN).

In the context of semantic search, the "distance" between two entities is the extent of similarity between them. The greater the distance, the less similar the entities are. A similarity search returns the entities which have the least distance from the queried entity.

You have several distance type options. The different distance or similarity measures can have different impacts on search results, and the best one to use can depend on the specific characteristics of the data and the problem you're trying to solve.

L2 (Euclidean Distance)

The L2 norm or Euclidean distance is probably the most commonly known metric. It represents the shortest straight line distance between two points in a space and is calculated as the square root of the sum of the squared differences between the corresponding elements of the two vectors.

IP (Inner Product)

The inner product (also known as the dot product) between two vectors is the sum of the products of their corresponding components. In the context of semantic search, inner product similarity is a measure of similarity that computes the dot product of the query vector and each document vector. High inner product values indicate high similarity. It's especially useful when dealing with vectors that represent things like user and item embeddings in recommendation systems.

COSINE (Cosine Similarity)

Cosine similarity measures the cosine of the angle between two vectors. This metric is not concerned with the magnitude of the vectors, only the angle between them, which makes it particularly useful when the "length" of the data (e.g., the number of words in a document) is not a relevant factor for the comparison. A cosine similarity of 1 means that the vectors have the same orientation (not necessarily the same magnitude), and a cosine similarity of -1 means they have opposite orientations.