Vector space modeling (VSM) is a mathematical model used in information retrieval (IR) where both documents and queries are represented as vectors in a multi-dimensional space. Each term in the vocabulary is associated with a dimension, and the value of each dimension corresponds to the importance or frequency of the term in the document or query. The goal is to measure the similarity between documents and queries by calculating the distance or angle between their vector representations.
In vector space modeling, terms are typically represented using methods like Term Frequency-Inverse Document Frequency (TF-IDF) or embeddings (like word2vec or GloVe). When a user submits a query, the system calculates the similarity between the query vector and the document vectors, ranking the documents based on their proximity to the query.
This model helps improve IR systems by enabling the comparison of documents that may not contain the exact query terms but are still contextually relevant, making it more effective than keyword-based retrieval. It is especially useful in handling synonyms and word variations.