Advanced Information Retrieval Techniques in the Big Data Era: Trends, Challenges, and Applications

The rapid expansion of Big Data has introduced novel opportunities and challenges for Information Retrieval (IR). This study examines the current state of IR techniques and their evolution to manage, organize, and derive meaningful insights from massive datasets. We explore how machine learning algorithms, deep learning models, and natural language processing (NLP) enhance data retrieval ac-curacy and velocity. A comprehensive analysis of contemporary methodologies indicates that per-sonalized search engines, e-commerce, and healthcare offer significant potential for improving re-trieval precision, scalability, and relevance. Furthermore, this study addresses critical ethical consid-erations, including data privacy and algorithmic bias, while exploring novel applications in autono-mous systems and personalized AI assistants. Advancing IR methodologies is vital in the Big Data era. Future research must focus on developing novel algorithmic procedures, integrating quantum computing, and establishing ethical AI practices. Ultimately, accelerating IR advancements is essen-tial to overcoming Big Data constraints and fostering technological innovation.

کلیدواژه ها:

Data privacy ، Algorithms ، AI Ethics ، Personalized search ، Semantic search ، Data analytics