"The Future of Search: Understanding Automated Information Retrieval Systems." This book looks at the evolution of automated information retrieval as well as future opportunities. It covers fundamental technology supporting contemporary search engines, recommendation systems, and artificial intelligence-driven information search. This book explores the fundamental concepts of TF-IDF, probabilistic retrieval, vector space models, and boolean search. Included also are more contemporary methods such as machine learning-based retrieval, neural search models, and contextual ranking algorithms. Emphasizing pragmatic application, the book examines how personalization, deep learning, and natural language processing (NLP) are transforming the traditional search experience. Among many other topics addressed are scalability, search result bias, and ethical concerns related to retrieval systems run by artificial intelligence. In a time of big data and artificial intelligence, this book provides insight into the new concepts altering search and information access. For fieldwork-based professionals and students in information science, students, developers, and researchers, it is ideal.