DSSM (Deep Structured Semantic Model) for Deep Semantic Similarity is a deep learning approach used to measure how closely two pieces of text match in meaning. It converts inputs like queries and documents into dense vector representations using neural networks. These vectors allow similarity to be calculated based on semantic meaning instead of exact keyword overlap. This improves the accuracy of search engines, recommendation systems, and NLP applications. DSSM captures contextual relationships between words and phrases to better understand intent. It is widely used in information retrieval and conversational AI systems. The model also improves over time by learning from user interaction data.
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