DSSM (Deep Structured Semantic Model) for Search and Ranking is a deep learning approach that improves how search engines understand user queries and rank relevant results. It converts both queries and documents into dense vector representations, enabling semantic matching based on meaning rather than exact keyword overlap. This helps search systems deliver more accurate and context-aware results. DSSM improves ranking quality by capturing deeper relationships between words, phrases, and intent. It is widely used in modern search engines, recommendation systems, and information retrieval platforms. It also enhances performance for ambiguous and long-tail queries. The model continuously improves relevance by learning from user interaction signals.
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