Consolidation of Diversifying Terms Weighting Impact on IR System Performances

Search engines and internet crawlers present automatically and accurately user s relevant data from a high dimensional words basket or databag which we named high dimensional vector space model, documents are stored into that large databag as a number of indexed vectors in the terms of spaces. When a document is searched, a query is given through the search engine. Mainly two major computing operations are done here, one for query vector and another document vector. The component of vectors is determined by the term weights, a function of the frequencies of the terms in the document or query as well as throughout the collection. So searching documents ultimately goes to the meaning of terms weighting into the high dimensional data space which is a difficult task in the data industries. Same times using a single method of terms weighting also suffers from certain limitations in application issues. So that highly diversified and consolidation of terms weighting approach can be applied as an interesting tool for improving retrieval performances. In this study, consolidation of diversifying terms weighting approach has been proposed as an argument of cost effective method for improving the retrieval performances. Under the proposed approach, a certain amount of Meta data has been tested and finally obtaining throughout results strongly recommend us that our approach is effective and has positive values, further applicable to promote retrieval performances.