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DSSM (Deep Structured Semantic Model) employs a deep neural network with a word hashing layer to project queries and documents into a shared low-dimensional semantic space, trained end-to-end on clickthrough data by maximising the posterior probability of clicked documents given queries. The model uses letter-trigram-based word hashing to reduce input dimensionality from 500K+ vocabulary terms to ~30K features, achieving statistically significant NDCG gains (~1-2% absolute) over BM25, LSA, and PLSA baselines in web search ranking tasks. This architecture enables ranking systems to overcome lexical mismatch between queries and documents - surfacing semantically relevant results where no keyword overlap exists - directly impacting relevance scoring layers in learning-to-rank pipelines without requiring manual feature engineering or query expansion modules.