A vector space model is a mathematical framework used to represent objects, such as documents or words, as vectors in a multi-dimensional space. Each dimension corresponds to a specific feature, allowing for the comparison of objects based on their characteristics. This model is commonly used in information retrieval and natural language processing to analyze and organize data. In a vector space model, the similarity between objects can be measured using various metrics, such as cosine similarity. This approach enables efficient searching and categorization, making it a fundamental concept in fields like machine learning and text mining.