DSSM (Deep Structured Semantic Model) for Information Retrieval is a deep learning approach that improves how systems match user queries with relevant documents. It converts both queries and documents into dense vector representations, enabling semantic matching based on meaning instead of exact keywords. This allows retrieval systems to handle ambiguous, short, or complex queries more effectively. DSSM improves ranking quality by capturing deeper relationships between terms. It is widely used in modern search engines to enhance relevance and user experience. It also helps bridge the gap between user intent and document representation. The model continuously improves results by learning from interaction patterns and feedback.
