چکیده (انگلیسی):

Vector space model allows computing a continuous degree of
similarity between queries and retrieved documents and then ranks the
documents in increasing order of cosine (similarity) value. It computes
cosine or similarity value using their cosine function. The cosine function
computes the similarity value by computing the weight of each term in the
documents using a weighting scheme but it is a complex process to compute
the weight of each term in the documents. It is also found that sometimes it
fails to compute a similarity score, Firstly if there is only one document in
the corpus and query terms match with the document and secondly, if the
number of documents containing query terms and total number of documents
retrieved are equal. To address this problem in order to improve the
performance, we proposed an enhanced approach for computation of cosine
or similarity value by enhancing the vector space model. Our work intends
to analyze and implement our proposed method in performance evaluation of
three search engines Google, Yahoo and MSN. To verify our method, we
compared our proposed method with a manually computed relevance score
and found that our evaluations match with manual method.