DSSM (Deep Structured Semantic Model) in Information Retrieval and Search Engines is a deep learning approach used to improve how search systems understand and match user queries with relevant documents. It converts both queries and documents into dense semantic vector representations using neural networks, enabling similarity matching based on meaning rather than exact keywords. This helps search engines deliver more accurate and context-aware results, even for ambiguous or complex queries. DSSM enhances ranking quality by capturing hidden relationships between words and phrases. It is widely used in modern search engines to improve relevance, scalability, and user satisfaction. It also helps reduce the gap between user intent and document representation in large-scale systems. Over time, it improves search performance by learning from user interactions and feedback signals.
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