Meta-Analysis on the Effectiveness of Personalized Recommendation Systems Using Inclusion and Exclusion Criteria

• Author(s): Adehi M. U. ; Adenomon M. O. ; Chaku S. E. ; Erewa-Lawani C. O.
• Paper ID: 1710535
• Page: 393-399
• Published Date: 09-09-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 3 September-2025

Abstract

This paper conducted a meta-analysis on the effectiveness of personalized recommendation systems using Inclusion and exclusion criteria. Dataset extraction was based on preferred reporting items for systematic reviews and meta-analyses through literature search and article selection. The meta-analysis was based on nine studies consisting of a total of 268,132 observations. The effect size was measured using standardized difference mean which was determined through a Google search. The random-effects model was employed for the analysis. The studies in the analysis were assumed to be a random sample from a universe of maternal mortality studies in Nigeria. The mean effect size was 1.566 with a 95% confidence interval of 1.194 to 2.053. The Z-value tested the null hypothesis that the mean effect size is 1. We found Z = 3.244 with p < 0.001 for ? = 0.05; hence, we rejected the null hypothesis and concluded that the mean effect size was not precisely 1 for personalized recommendation systems. The Q-statistic provided a test of the null hypothesis that nine studies in the analysis share a common effect size; the Q-value is 15.97 with 8 degrees of freedom (k-1) and p < 0.001. For ? = 0.1, we rejected the null hypothesis that the true effect size was the same in all the 9 studies since Q=k-1, k being the number of studies. The I-squared statistic was 65.3%, which tells us that some 65.3% of the variance in observed effects reflected variance in true effects rather than sampling error. Tau-squared, the variance of true effect sizes, was 0.114 in log units. The study recommended that there should be personalized controlled plans, this will help optimize outcomes and reduce the occurrence of severe mean effects.

Keywords

Meta-analysis; Inclusion and Exclusion Criteria; Effect Size; I2 statistic; Q-test.

Citations

IRE Journals:
Adehi M. U. , Adenomon M. O. , Chaku S. E. , Erewa-Lawani C. O. "Meta-Analysis on the Effectiveness of Personalized Recommendation Systems Using Inclusion and Exclusion Criteria" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 393-399

IEEE:
Adehi M. U. , Adenomon M. O. , Chaku S. E. , Erewa-Lawani C. O. "Meta-Analysis on the Effectiveness of Personalized Recommendation Systems Using Inclusion and Exclusion Criteria" Iconic Research And Engineering Journals, 9(3)