@Article{crph6036,
AUTHOR = {Naayini, Prudhvi and Jonnalagadda, Anil Kumar and Kamatala, Srikanth},
TITLE = {Advancing Preference Learning in AI: Beyond Pairwise Comparisons},
JOURNAL = {Current Research in Public Health},
VOLUME = {4},
YEAR = {2025},
NUMBER = {1},
PAGES = {15-19},
URL = {https://www.scipublications.com/journal/index.php/UJCSC/article/view/6036},
ISSN = {2831-5162},
DOI = {10.31586/ujcsc.2025.6036},
ABSTRACT = {Preference learning plays a crucial role in AI applications, particularly in recommender systems and personalized services. Traditional pairwise comparisons, while foundational, present scalability challenges in large-scale systems. This study explores alternative elicitation methods such as ranking, numerical ratings, and natural language feedback, alongside a novel hybrid framework that dynamically integrates these approaches. The proposed methods demonstrate improved efficiency, reduced cognitive load, and enhanced accuracy. Results from simulated user studies reveal that hybrid approaches outperform traditional methods, achieving a 40% reduction in user effort while maintaining high predictive accuracy. These findings open pathways for deploying user-centric, scalable preference learning systems in dynamic environments.},
}

%0 Journal Article
%A Naayini, Prudhvi
%A Jonnalagadda, Anil Kumar
%A Kamatala, Srikanth
%D 2025
%J Current Research in Public Health

%@ 2831-5162
%V 4
%N 1
%P 15-19

%T Advancing Preference Learning in AI: Beyond Pairwise Comparisons
%M doi:10.31586/ujcsc.2025.6036
%U https://www.scipublications.com/journal/index.php/UJCSC/article/view/6036

TY  - JOUR
AU  - Naayini, Prudhvi
AU  - Jonnalagadda, Anil Kumar
AU  - Kamatala, Srikanth
TI  - Advancing Preference Learning in AI: Beyond Pairwise Comparisons
T2  - Current Research in Public Health
PY  - 2025
VL  - 4
IS  - 1
SN  - 2831-5162
SP  - 15
EP  - 19
UR  - https://www.scipublications.com/journal/index.php/UJCSC/article/view/6036
AB  - Preference learning plays a crucial role in AI applications, particularly in recommender systems and personalized services. Traditional pairwise comparisons, while foundational, present scalability challenges in large-scale systems. This study explores alternative elicitation methods such as ranking, numerical ratings, and natural language feedback, alongside a novel hybrid framework that dynamically integrates these approaches. The proposed methods demonstrate improved efficiency, reduced cognitive load, and enhanced accuracy. Results from simulated user studies reveal that hybrid approaches outperform traditional methods, achieving a 40% reduction in user effort while maintaining high predictive accuracy. These findings open pathways for deploying user-centric, scalable preference learning systems in dynamic environments.
DO  - Advancing Preference Learning in AI: Beyond Pairwise Comparisons
TI  - 10.31586/ujcsc.2025.6036
ER  -