Algorithm for information retrieval optimization

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Kehinde Kayode Agbele

Elizade University, Nigeria

: J Comput Eng Inf Technol

Abstract

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When using Information Retrieval (IR) systems, users often present search queries made of ad-hoc keywords. It is then up to information retrieval systems (IRS) to obtain a precise representation of user’s information need, and the context of the information. This paper investigates optimization of IRS to individual information needs in order of relevance. The study addressed development of algorithms that optimize the ranking of documents retrieved from IRS. This study discusses and describes a Document Ranking OPTimization (DROPT) algorithm for information retrieval (IR) in a web-based or designated databases environment. Conversely, as the volume of information available online and in designated databases are growing continuously, ranking algorithms can play a major role in the context of search results. In this paper, a DROPT technique for documents retrieved from a corpus is developed with respect to document index keywords and the query vectors. This is based on calculating the weight (wij) of keywords in the document index vector, calculated as a function of the frequency of a keyword kj across a document di. The purpose of DROPT technique is to reflect how human users can judge the context changes in IR result rankings according to information relevance. Our technique for IR optimization is generic for ranking retrieved documents. This paper found out that it is possible for DROPT technique to overcome some of the limitations of existing traditional (tf×idf) algorithms via adaptation. A context-based IRS is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behaviour to improve the IR effectiveness.

Biography

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Email: agbelek@gmail.com