Open AccessCorrelation and prediction of high-cost information retrieval evaluation metrics using deep learning
Author(s) -
Sinyinda Muwanei,
Sri Devi Ravana,
Wai Lam Hoo,
Douglas Kunda,
Prabha Rajagopal,
Prabhpreet Singh Sodhi
Publication year - 2022
Publication title -
information research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.397
H-Index - 49
ISSN - 1368-1613
DOI - 10.47989/irpaper920
Subject(s) - computer science , metric (unit) , relevance (law) , correlation , precision and recall , rank correlation , artificial intelligence , pearson product moment correlation coefficient , data mining , regression , information retrieval , correlation coefficient , rank (graph theory) , machine learning , spearman's rank correlation coefficient , learning to rank , recall , statistics , ranking (information retrieval) , mathematics , combinatorics , linguistics , operations management , geometry , philosophy , political science , law , economics
. To reduce cost of the evaluation of information retrieval systems, this study proposes a method that employs deep learning to predict the precision evaluation metric. It also aims to show why some of existing evaluation metrics correlate with each other while considering the varying distributions of relevance assessments. It aims to ensure reproducibility of all the presented experiments. Method. Using data from several test collections of the Text REetrieval Conference (TREC) we show why some evaluation metrics correlate with each other, through mathematical intuitions. In addition, regression models were used to investigate how the predictions of the evaluation metrics are affected by queries or topics with variations of relevance assessments. Lastly, the proposed prediction method employs deep learning. Analysis. We use coefficient of determination, Kendall's tau, Spearman and Pearson correlations. Results. This study showed that the proposed method performed better predictions than other recently proposed methods in retrieval research. It also showed why the correlation exists between precision and rank biased precision metrics, and why recall and average precision metrics have reduced correlation when the cut-off depth increases. Conclusions. The proposed method and the justifications for the correlations between some pairs of retrieval metrics will be valuable to researchers for the predictions of the evaluation metrics of information retrieval systems.