Performance Metrics Analysis for Deep Learning Models

Evaluating an Information Retrieval (IR) model is a multi-faceted process that requires selecting the right quantitative metrics to assess performance. The selection of evaluation metrics in IR depends on specific tasks, relevant standards, and desired characteristics. To thoroughly assess an IR system’s performance, a combination of metrics is necessary. This study explores the effectiveness of deep learning (DL) models in semantic and personalized information retrieval (SIR), focusing on BERT and other large language models (LLMs) that provide context-sensitive embeddings and advanced language comprehension capabilities. Through a detailed analysis, this paper demonstrates how DL models can significantly enhance accuracy and relevance in IR, using evaluation metrics critical for assessing model performance. Metrics like recall, precision, and F1-score are key in capturing model accuracy and coverage; for example, a recall of $\mathbf{1. 0}$ indicates complete retrieval of relevant instances, while a precision of 0.6 reflects a $\mathbf{6 0 \%}$ accuracy in positive predictions, balancing these measures at an F1-score of 46.15%. Ranking and prioritization metrics, such as Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG), evaluated at 91.67% and 95.1%, respectively highlight the models’ capabilities in prioritizing relevant results effectively. In personalized and semantic search, selecting the right evaluation metrics is essential to maximize DL model potential and improve the user experience. Recent LLM advancements, like GPT-4, are instrumental in capturing nuanced meanings and understanding complex user queries, which enhances the accuracy of search engines. Continuous optimization of these models through tailored metrics can improve IR systems’ contextual relevance and accuracy, fostering greater user satisfaction.