Title: Consolidation
of Diversifying Terms Weighting Impact on IR System Performances
Abstract: Search
engines and internet crawlers present automatically and accurately user’s
relevant data from a high dimensional “words basket” 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 space. 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
paper, 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