Deep Structured Semantic Models (DSSM) are deep learning models designed to learn semantic relationships between different types of text, such as user queries and documents. They convert text into dense vector representations using neural networks so that similar meanings are placed closer in vector space. This enables matching based on meaning instead of exact word overlap. DSSM is widely used in search engines, recommendation systems, and chatbots to improve relevance and intent understanding. It helps handle ambiguous queries and improves ranking and retrieval quality. The model also learns from user interactions to refine future results. It is effective in large-scale systems where traditional keyword matching falls short.
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