Neural Network Architecture for DSSM (Deep Structured Semantic Model) is designed to learn semantic relationships between text inputs such as queries and documents using deep learning. It uses feedforward neural networks to convert text into dense vector representations in a shared semantic space. This allows the model to measure similarity based on meaning rather than exact keyword overlap. The architecture typically includes multiple hidden layers that capture complex patterns and contextual relationships in text. It improves search relevance, ranking accuracy, and matching quality in retrieval systems. The model is widely used in search engines, recommendation systems, and conversational AI. It also adapts over time by learning from user interaction data.
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