Rahat Iqbal

Work place: Department of Computing, Coventry University,Priory Street, CV1 5FB Coventry, UK



Research Interests: Data Structures and Algorithms, Information Retrieval


Dr. Iqbal serves on programme committee and an advisory committee of many international conferences and journals. He has published more 100 papers in international journals, conferences, book chapters and workshops. He has supervised to completion 12 PhD students in information retrieval, health shock prediction, disaster management, emotions modelling, industrial automation and fault detection. He is currently a Reader in Human-Centred Technology in the School of Computing, Electronics and Mathematics at Coventry University. His main research interests lie in Big Data Analytics and Information Retrieval.

Author Articles
Development of Relevance Feedback System using Regression Predictive Model and TF-IDF Algorithm

By Stephen Akuma Rahat Iqbal

DOI: https://doi.org/10.5815/ijeme.2018.04.04, Pub. Date: 8 Jul. 2018

Domain-specific retrieval systems developed for a homogenous group of users can potentially optimise the recommendation of relevant web documents in minimal time as compared to generic systems built for a heterogeneous group of users. Domain-specific retrieval systems are normally developed by learning from users’ past interactions, as a group or individual, with an information system. This paper focuses on the recommendation of relevant web documents to a cohort of users based on their search behaviour. Simulated task situations were used to group users of the same domain. The motivation behind this work is to help a cohort of users find relevant documents that will satisfy their information needs effectively. An aggregated implicit predictive model derived from correlating implicit and explicit feedback parameters was integrated with the traditional term frequency/inverse document frequency (tf-idf) algorithm to improve the relevancy of retrieval results. The aggregated model system was evaluated in terms of recall and precision (Mean Average Precision) by comparing it with self-designed retrieval system and a generic system. The performance of the three systems was measured based on the relevant documents returned. The results showed that the aggregated domain-specific system performed better in returning relevant documents as compared to the other two systems.

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