DSSM (Deep Structured Semantic Model) for Search and Ranking is a deep learning approach used to improve how search systems match and order results based on meaning. It converts queries and documents into dense vector representations, allowing similarity to be measured beyond exact keyword matches. This helps search engines deliver more relevant and context-aware results. DSSM improves ranking accuracy by understanding semantic relationships between terms and phrases. It also handles ambiguous and long-tail queries more effectively. The model learns from user interactions to continuously refine search relevance and ranking quality.
