DSSM (Deep Structured Semantic Model) for Large-Scale Semantic Matching is a deep learning approach used to match queries and documents based on meaning across massive datasets. It converts text into dense vector representations using neural networks, enabling similarity measurement beyond exact keyword overlap. This helps systems efficiently retrieve relevant results even in high-volume search environments. DSSM improves ranking accuracy by capturing semantic relationships between words, phrases, and user intent. It is widely used in search engines, recommendation systems, and NLP applications. The model scales effectively with distributed computing and large datasets. It also improves continuously by learning from user interaction data.
Showing the single result
