Review of Information Retrieval: Models, Performance Evaluation Techniques and Applications

Information Retrieval (IR) is a field that concerns the structure, memory, analysis, and access to pieces of information. It has a wide application in various areas like search engines, communication systems, information filtering, medical search, etc., and helps design efficient and secure applications. This area has been a surge of research from the last few years due to data mining's unparalleled success, deep learning in computer vision, blockchain technology, etc. Core models, performance evaluation techniques, IR system applications, and its role in blockchain technology have been proposed in this literature, calling the need for a broad survey to focus the research in this promising area. This paper fills the space by surveying the state of art approaches with deep learning models, query expansion techniques used, and the use of private information retrieval in blockchain technology. This survey paper includes different IR models like boolean model, vector space model, probabilistic model, language model, N-gram model, fuzzy model, Latent Semantic Indexing (LSI) Model, Bayesian network, Evolutionary algorithm-based models and Machine Learning based models. Applications of IR systems along with different datasets are also included to provide further research in this field.