A structured model for evaluation of information retrieval (IR).

Area of Science:

• Information Science
• Computer Science
• Data Science

Background:

• Current information retrieval (IR) evaluation methods, particularly those based on relevance metrics like recall and precision, have limitations.
• A clear distinction between the IR process and the technical IR system is often overlooked in evaluations.

Purpose of the Study:

• To review existing information retrieval (IR) evaluation methodologies.
• To propose a novel model for evaluating the IR process, differentiating it from the underlying technical system.
• To provide a framework for structured evaluation, planning, analysis, and comparison of IR experiments.

Main Methods:

• Literature review of existing IR evaluation techniques.
• Conceptualization and proposal of a new IR process-centric evaluation model.
• Emphasis on distinguishing the human-centric IR process from the technology-centric IR system.

Main Results:

• Identified limitations in traditional recall and precision-based IR evaluation.
• Development of a new conceptual model for IR evaluation that separates the process from the system.
• Establishment of a basis for more structured and comprehensive experimental design and analysis in IR.

Conclusions:

• The proposed model offers a more nuanced approach to understanding and evaluating information retrieval.
• Separating the IR process from the system allows for more targeted improvements and assessments.
• This framework facilitates better planning, analysis, and comparison of diverse IR methodologies and experimental outcomes.