JOURNAL ARTICLE

Evaluating the effectiveness of information retrieval systems using effort-based relevance judgment

Prabha RajagopalSri Devi RavanaYun Sing KohVimala Balakrishnan

Year: 2018 Journal:   Aslib Journal of Information Management Vol: 71 (1)Pages: 2-17   Publisher: Emerald Publishing Limited

Abstract

Purpose The effort in addition to relevance is a major factor for satisfaction and utility of the document to the actual user. The purpose of this paper is to propose a method in generating relevance judgments that incorporate effort without human judges’ involvement. Then the study determines the variation in system rankings due to low effort relevance judgment in evaluating retrieval systems at different depth of evaluation. Design/methodology/approach Effort-based relevance judgments are generated using a proposed boxplot approach for simple document features, HTML features and readability features. The boxplot approach is a simple yet repeatable approach in classifying documents’ effort while ensuring outlier scores do not skew the grading of the entire set of documents. Findings The retrieval systems evaluation using low effort relevance judgments has a stronger influence on shallow depth of evaluation compared to deeper depth. It is proved that difference in the system rankings is due to low effort documents and not the number of relevant documents. Originality/value Hence, it is crucial to evaluate retrieval systems at shallow depth using low effort relevance judgments.

Keywords:
Relevance (law) Information retrieval Computer science Readability Outlier Ranking (information retrieval) Grading (engineering) Originality Data mining Artificial intelligence Psychology

Metrics

2
Cited By
0.89
FWCI (Field Weighted Citation Impact)
22
Refs
0.81
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Information Retrieval and Search Behavior
Physical Sciences →  Computer Science →  Information Systems
Advanced Text Analysis Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Mobile Crowdsensing and Crowdsourcing
Physical Sciences →  Computer Science →  Computer Science Applications

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