Word Embedding

What is Word Embedding?

A word embedding is a type of vector representation where words are mapped into continuous multi-dimensional space. Words with similar meaning (king, queen) are placed close together, enabling semantic similarity in search and NLP.

How It Works (quick)

• Training: Models like Word2Vec, GloVe, or fastText learn embeddings from large corpora.
• Representation: Each word → dense vector of real numbers.
• Similarity: Compare vectors with cosine similarity.
• Use cases: Synonym detection, query expansion, semantic ranking.

Why It Matters in E-commerce

• Helps match queries like “hoodie” with products labeled “sweatshirt”.
• Improves long-tail query recall.
• Powers recommendations and cross-language search.

Best Practices

• Use domain-specific embeddings (trained on product data).
• Regularly retrain to keep vocabulary fresh.
• Combine embeddings with lexical search (hybrid).
• Monitor for biases in training data.

Summary

Word embeddings capture meaning in numbers, enabling semantic search and smarter product discovery.