What question did this study set out to answer?

Determine the effort required to locate information within relevant documents using machine learning and evaluate how integrating findability effort affects information retrieval performance metrics.

January 6, 2023International Journal of Information Retrieval ResearchOpen Access

Clustering of Relevant Documents Based on Findability Effort in Information Retrieval

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Determine the effort required to locate information within relevant documents using machine learning and evaluate how integrating findability effort affects information retrieval performance metrics.
Extracted findability features from retrieved relevant documents using natural language processing techniques.
Applied unsupervised k-means clustering to categorize relevant documents according to the amount of effort needed to find target information.
Relevant retrieved documents were successfully separated into distinct effort tiers using k-means clustering.
Information retrieval system performance shifted by an average of 23% when accounting for document findability effort.

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Implication

Algorithmic evaluation demonstrates a 23% performance variation across information retrieval systems, indicating that accounting for user findability effort alters assessment accuracy.

Cite This Study

Rajagopal et al. (2023) studied this question.

synapsesocial.com/papers/6a6f58bd6c240de38cdb72ab https://doi.org/10.4018/ijirr.315764

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Implication

Algorithmic evaluation demonstrates a 23% performance variation across information retrieval systems, indicating that accounting for user findability effort alters assessment accuracy.

Clustering of Relevant Documents Based on Findability Effort in Information Retrieval

Key Points

Discussion

Implication

Cite This Study

Clustering of Relevant Documents Based on Findability Effort in Information Retrieval

Key Points

Discussion

Implication

Cite This Study

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