A Particle Swarm Optimization Algorithm for Web Information Retrieval: A Novel Approach

A Particle Swarm Optimization Algorithm for Web Information Retrieval: A Novel Approach

Tarek Alloui (MISC Laboratory, Department of Computer Science and its Applications, University Constantine 2 Abdelhamid Mehri, Constantine, Algeria), Imane Boussebough (LIRE Laboratory, Department of Software Technology and Information Systems, University Constantine 2 Abdelhamid Mehri, Constantine, Algeria) and Allaoua Chaoui (MISC Laboratory, Department of Computer Science and its Applications, University Constantine 2 Abdelhamid Mehri, Constantine, Algeria)
Copyright: © 2015 |Pages: 15
DOI: 10.4018/IJIIT.2015070102

Abstract

The Web has become the largest source of information worldwide and the information, in its various forms, is growing exponentially. So obtaining relevant and up-to-date information has become hard and tedious. This situation led to the emergence of search engines which index today billions of pages. However, they are generic services and they try to aim the largest number of users without considering their information needs in the search process. Moreover, users use generally few words to formulate their queries giving incomplete specifications of their information needs. So dealing this problem within Web context using traditional approaches is vain. This paper presents a novel particle swarm optimization approach for Web information retrieval. It uses relevance feedback to reformulate user query and thus improve the number of relevant results. In the authors' experimental results, they obtained a significant improvement of relevant results using their proposed approach comparing to what is obtained using only the user query into a search engine.
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Query Reformulation

Query reformulation is a process which generates, from the initial user query, a new query more appropriate to his needs. There are two main approaches: query expansion (Araujo and Pérez-Agüera, 2008; Chang et al., 2003; Qiu and Frei, 1993) and reweighting query keywords (Horng et al., 2003; Kim et al., 2001; Kraft et al., 1994).

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