Analysis of Vector Space Model in Information Retrieval

Information retrieval is great technology behind web search services. In information retrieval, it is common to model index terms and documents as vectors in a suitably defined vector space. The vector space model is one of the classical and widely applied retrieval models to evaluate relevance of web page. The retrieval operation consists of computing the cosine similarity function between a given query vector and the set of documents vector and then ranking documents accordingly. In this paper, we present different approaches of vector space model to compute similarity score of hits from search engine and more importantly, it is felt that this investigation will lead to a clearer understanding of the issues and problems in using the vector space model in information retrieval and our work intends to discuss the main aspects of Vector space models and provide a comprehensive comparison for TermCount model, Tf-Idf model and Vector space model based on normalization.