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

References

[1] Adehi M.U and David I.J. Corrections as effect sizes in Meta-Analysis. Science and Technology 2024, 14(1):1 -3. 10.5923/J.scit.20241401.01

[2] Aher SB, Lobo LMRJ. Combination of machine learning algorithms for recommendation of courses in e-learning System based on historical data. Knowl-Based Syst. 2013;51:1–14.

[3] Bakshi S, Jagadev AK, Dehuri S, Wang GN. Enhancing scalability and accuracy of recommendation systems using unsupervised learning and particle swarm optimization. Appl Soft Comput. 2014;15:21–9. 24. Kim Y, Shim K. TWILITE: A recommendation system for twitter using a probabilistic model based on latent Dirichlet allocation. Inf Syst. 2014;42:59–77.

[4] Borenstein, M. (2019). Common Mistakes in Meta-Analysis and How to Avoid Them. Biostat, Inc.

[5] Borenstein, M. (2020). Research Note: In a meta- analysis, the I2 index does not tell us how much the effect size varies across studies. J Physiother, 66(2), 135 -139. https://

[6] DerSimonian, R., & Laird, N. (2015). Meta- analysis in clinical trials revisited. Contemp Clin Trials, 45(Pt A), 139 -145. https://

[7] Dong H, Hussain FK, Chang E. A service concept recommendation system for enhancing the dependability of semantic service matchmakers in the service ecosystem environment. J Netw Comput Appl. 2011;34:619–31.

[8] García-Crespo Á, López-Cuadrado JL, Colomo- Palacios R, González-Carrasco I, Ruiz-Mezcua B. Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain. Expert Syst Appl. 2011;38:13310–9.

[9] Gemmell J, Schimoler T, Mobasher B, Burke R. Resource recommendation in social annotation systems: A linearweighted hybrid approach. J Comput Syst Sci. 2012;78:1160–74.

[10] Gottschlich J, Hinz O. A decision support system for stock investment recommendations using collective wisdom. Decis Support Syst. 2014;59:52–62.

[11] Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. Academic Press. Publisher description http://www.loc.gov/catdir/description/els032/84 012469.html

[12] Hedges, L. V., & Vevea, J. L. (1998). Fixed and random-effects models in meta-analysis. Psychological Methods, 3(4), 486-504.

[13] Liu L, Xu J, Liao SS, Chen H. A real-time personalized route recommendation system for self-drive tourists based on vehicle to vehicle communication. Expert Syst Appl. 2014;41:3409–17.

[14] Lorenzi F, Bazzan ALC, Abel M, Ricci F. Improving recommendations through an assumption-based multiagent approach: An application in the tourism domain. Expert Syst Appl. 2011;38:14703–14.

[15] Lucas JP, Luz N, Moreno MN, Anacleto R, Figueiredo AA, Martins C. A hybrid recommendation approach for a tourism system. Expert Syst Appl. 2013;40:3532–50.

[16] Mohanraj V, Chandrasekaran M, Senthilkumar J, Arumugam S, Suresh Y. Ontology driven bee’s foraging approach based self-adaptive online recommendation system. J Syst Softw. 2012;85:2439–50. 12. Hsu CC, Chen HC, Huang KK, Huang YM. A personalized auxiliary material recommendation system based on learning style on facebook applying an artifcial bee colony algorithm. Comput Math Appl. 2012;64:1506–13.

[17] Niu J, Zhu L, Zhao X, Li H. Afvir: An afect- based Internet video recommendation system. Neurocomputing. 2013;120:422–33.

[18] Rawat YS, Kankanhalli MS. ClickSmart: A context-aware viewpoint recommendation system for mobile photography. IEEE Trans Circuits Syst Video Technol. 2017;27:149–58.

[19] Rice, K., Higgins, J. P. T., & Lumley, T. (2017). A re-evaluation of fixed effect(s) meta-analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society), n/a -n/a. https://

[20] Sankar CP, Vidyaraj R, Kumar KS. Trust based stock recommendation system – a social network analysis approach. Procedia Computer Sci. 2015;46:299–305.

[21] Yang S, Korayem M, Aljadda K, Grainger T, Natarajan S. Combining content-based and collaborative fltering for job recommendation system: A cost-sensitive Statistical Relational Learning approach. Knowl-Based Syst. 2017;136:37–45.

[22] Yeh DY, Cheng CH. Recommendation system for popular tourist attractions in Taiwan using delphi panel and repertory grid techniques. Tour Manage. 2015;46:164–76.

[23] Zahálka J, Rudinac S, Worring M. Interactive multimodal learning for venue recommendation. IEEE Trans Multimedia. 2015;17:2235–44.

@article{1710535,
      author = {Adehi M. U., Adenomon M. O., Chaku S. E., Erewa-Lawani C. O.},
      title = {Meta-Analysis on the Effectiveness of Personalized Recommendation Systems Using Inclusion and Exclusion Criteria},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {3},
      pages = {393-399},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710535.pdf},
      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.},
      month = {September},
  }