Embedding Hybrid Evolutionary Approach for Learning-to-Rank Computation for the Selection of Features Using Machine Learning

Abstract

Our study proposes a novel model for retrieving objects that utilizes learning-to-rank with L2 regularization. We employed an evolutionary-based simulated annealing technique to select the most informative features for our system and utilized a standardized regulation technique to handle the dropout of active features. Learning to rank is a well-researched area in machine learning and finds application in recommendation systems and search engines. Our study aims to introduce a new approach to feature selection for the learning-to-rank information retrieval model. By dropping inactive features and maintaining active features, we can improve the ranking function’s performance. We tested our proposed method on standard datasets and repeatedly improved the feature selection model of the LambdaMart algorithm. Empirical performance results show that our heuristic model provides better feature subset combinations, as measured by the NDCG, P@10, and MAP evolutionary metrics, than do baseline databases. Our proposed method surpasses existing learning-to-rank methods, paving the way for promising future research.