Information Retrieval Questions Medium
The Vector Space Model (VSM) is a mathematical model used in information retrieval to represent and rank documents based on their relevance to a given query. It treats both documents and queries as vectors in a high-dimensional space, where each dimension represents a unique term or feature.
In the VSM, each document is represented as a vector, with the length of the vector equal to the total number of unique terms in the entire document collection. The value of each dimension in the vector corresponds to the frequency or weight of the corresponding term in the document. Similarly, the query is also represented as a vector, where each dimension represents the frequency or weight of the terms in the query.
To determine the relevance of a document to a query, the VSM calculates the similarity between the document vector and the query vector using various similarity measures, such as cosine similarity. The higher the similarity score, the more relevant the document is considered to be.
The VSM allows for efficient retrieval of relevant documents by ranking them based on their similarity scores. It is widely used in search engines, document classification, and recommendation systems. However, the VSM has limitations, such as the inability to capture the semantic meaning of terms and the reliance on term frequency as the sole measure of importance.