Sparse Representations for Information Retrieval

Sparse representations for information retrieval involve representing documents and queries using only the most relevant keywords or features, resulting in many zeros in their data vectors. This approach emphasizes important terms and reduces noise, making it easier for search systems to match queries with relevant documents efficiently. By focusing on key features, sparse representations improve search accuracy and speed, especially when dealing with large datasets. Think of it as highlighting essential words in a document so the system can quickly identify relevant information without getting distracted by less important details.