DSSM (Deep Structured Semantic Model) for Semantic Matching is a deep learning approach used to understand and compare the meaning of text inputs like queries and documents. It converts text into dense vector representations so semantic similarity is measured by meaning instead of exact keyword overlap. This improves matching accuracy in search engines, recommendation systems, and chatbots. It also handles variations in language, phrasing, and ambiguity more effectively. DSSM enhances relevance by capturing contextual relationships between words and phrases. It learns from user behavior to improve future matching quality.
