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<p>In the realm of information retrieval (IR) and multi-criteria decision-making (MCDM), the selection of an appropriate similarity measure is crucial for evaluating keyword significance within large corpora and understanding user behavior. This study explores two foundational methodologies for document ranking: term frequency-inverse document frequency (TF-IDF) and best matching 25 (BM25). These methods provide key insights into the efficiency of information retrieval systems. Additionally, this research introduces an innovative similarity metric grounded in intuitionistic fuzzy credibility (IFC) principles, offering a deeper perspective on document context. Furthermore, the study presents a unique aggregation operator, the intuitionistic fuzzy credibility weighted aggregation (IFCWA) operator, employing the entropy weight method (EWM) to assign weights to decision-makers and criteria. It addresses critical gaps in current assessment methodologies by delving into the pivotal role of these methodologies in assessing keyword significance within textual datasets and their implications for user behavior within the Amazon marketplace. As the digital marketplace continues to evolve, our research aims to bridge critical gaps in keyword significance assessment methodologies, contributing significantly to the development of more sophisticated and effective information retrieval systems that enhance user engagement and decision-making processes.</p>

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An Intuitionistic Fuzzy Credibility Model for Exploring Document Ranking Through Similarity Measures in Information Retrieval Systems

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • Rukhsana Kausar,
  • Malaika,
  • Muhammad Riaz,
  • Vladimir Simic,
  • Dragan Pamucar

摘要

In the realm of information retrieval (IR) and multi-criteria decision-making (MCDM), the selection of an appropriate similarity measure is crucial for evaluating keyword significance within large corpora and understanding user behavior. This study explores two foundational methodologies for document ranking: term frequency-inverse document frequency (TF-IDF) and best matching 25 (BM25). These methods provide key insights into the efficiency of information retrieval systems. Additionally, this research introduces an innovative similarity metric grounded in intuitionistic fuzzy credibility (IFC) principles, offering a deeper perspective on document context. Furthermore, the study presents a unique aggregation operator, the intuitionistic fuzzy credibility weighted aggregation (IFCWA) operator, employing the entropy weight method (EWM) to assign weights to decision-makers and criteria. It addresses critical gaps in current assessment methodologies by delving into the pivotal role of these methodologies in assessing keyword significance within textual datasets and their implications for user behavior within the Amazon marketplace. As the digital marketplace continues to evolve, our research aims to bridge critical gaps in keyword significance assessment methodologies, contributing significantly to the development of more sophisticated and effective information retrieval systems that enhance user engagement and decision-making processes.