Embedding
Core Facts
Timeline (last 90 days)
Embedding-based query deduplication encodes the SQL or the parsed intent into a vector before query execution, retrieves whether there is a semantically equivalent recent request, and returns the cached result if the underlying parameters match within the freshness window.
One-hot encoding produces a high-dimensional sparse matrix; when the cardinality of categories is very large, target encoding or embeddings should be used instead.
Matrix factorization decomposes a user-item interaction matrix into the product of two low-rank matrices, capturing latent features of users and items in a latent space.
All Facts (4)
Vector embedding is the process of transforming discrete text, code, or experiences into numerical vectors in a continuous high-dimensional space, so that semantically similar content is closer in vector space.
70%UnverifiedMatrix factorization decomposes a user-item interaction matrix into the product of two low-rank matrices, capturing latent features of users and items in a latent space.
60%UnverifiedEmbedding-based query deduplication encodes the SQL or the parsed intent into a vector before query execution, retrieves whether there is a semantically equivalent recent request, and returns the cached result if the underlying parameters match within the freshness window.
50%UnverifiedOne-hot encoding produces a high-dimensional sparse matrix; when the cardinality of categories is very large, target encoding or embeddings should be used instead.
50%