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

Stephen Shiaondo Cyril Akuma, Rahat Iqbal

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    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
    Issue number4
    Publication statusPublished - 8 Jul 2018

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


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