Why can Embeddings improve search accuracy?
Embeddings enhance search accuracy by representing text or items as dense numerical vectors in a semantic space. This captures contextual meaning and similarity far better than simple keyword matching.
These vectors position semantically similar items close together and dissimilar ones farther apart in the vector space. Consequently, a search query can also be embedded, enabling finding similar documents through fast, efficient vector similarity calculation (e.g., cosine similarity). This approach understands context, synonyms, and related concepts, overcoming the limitations of exact word matches. Dimensionality reduction inherent in embeddings focuses on meaningful semantic features.
Consequently, embedding-based search excels at handling natural language queries, finding documents sharing conceptual similarity even without verbatim keyword overlap, and powering relevant recommendations. It is foundational to semantic search, improving recall and relevance across applications like e-commerce product discovery and document retrieval.
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