DSSM for Query-Document Matching (Deep Structured Semantic Model) is a deep learning method used in information retrieval systems to measure semantic similarity between user queries and documents. It converts queries and documents into dense vector representations using neural networks so the system can understand meaning instead of relying on exact keyword matches. This improves search relevance, ranking accuracy, and user intent matching in large-scale search engines and recommendation systems. It also helps search systems handle unclear or complex queries more effectively. The model learns from user interactions and continuously improves retrieval quality over time.
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