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

Stephen Shiaondo Cyril Akuma, Rahat Iqbal

Research output: Contribution to journalArticle

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Abstract

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.
Original languageEnglish
Pages (from-to)31
Number of pages49
JournalInternational journal of Education and Management Engineering
Volume8
Issue number4
DOIs
Publication statusPublished - 8 Jul 2018

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All articles published by MECS are made immediately available worldwide under the Creative Commons Attribution 4.0 International License.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

  • Recommender System
  • Implicit feedback system
  • Domain-specific retrieval
  • information retrieval
  • search engine

Cite this

Development of Relevance Feedback System using Regression Predictive Model and TF-IDF Algorithm. / Akuma, Stephen Shiaondo Cyril; Iqbal, Rahat.

In: International journal of Education and Management Engineering, Vol. 8, No. 4, 08.07.2018, p. 31.

Research output: Contribution to journalArticle

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