A Vector space model is an algebraic model for representing text documents as vectors of identifiers. A possible use for a vector space model is for retrieval and filtering of information. Other possible uses for vector space models are indexing and also to rank the relevancy of differing documents.
To explain further vector space models, basically a document is characterized by a vector. With each separate term corresponding to the differing dimensions. There has been multiple ways of trying to compute the different possible values for vector space models with the most recognised being the tf-idf weighting.
The differing application has a direct influence on what the definition of the term means. A normal term is usually a single word, keywords or longer phrases. The number of unique words in the vocabulary denotes the dimensionality, if words are used for the terms.
However whilst vector space modelling is useful there are 4 key problems with using it, they are; that the order of the terms are lost, keywords must be precise if searched for, bigger documents have a poor similarity value due to being poorly represented and two documents based on the same topic won’t be associated if term vocabulary differs.