Personalized Information Retrieval Using LETOR Machine Learning Re-Ranking Algorithms in MSLR-WEB10K Dataset: A Comprehensive Study

Abstract

Personalized Information Retrieval (PIR) aims to tailor search results to individual user preferences and contexts. With the exponential growth of digital information, traditional retrieval systems often fall short in delivering relevant results to diverse users. This study explores the integration of machine learning re-ranking algorithms into personalized information retrieval systems to enhance search relevance and user satisfaction. The LETOR based model is experimented for relevance re-ranking in personalized retrieval. A comprehensive analysis of LETOR based models are analyzed to find a best hybrid re-ranking framework based on the performance of each model. The findings demonstrate that the LGBMRegressor model demonstrated the most consistent and best performance across the majority of metrics.