Advanced Information Retrieval System: Theoretical and Experimental Perspective blends foundational theory with practicality to provide an integrative exploration of modern information retrieval (IR) systems. This volume examines a wide range of IR methodologies, from classical indexing and ranking techniques to cutting-edge AI-driven approaches, demonstrating how these systems can be applied across diverse domains, including web search, recommendation systems, sentiment analysis, and multimedia retrieval.The book takes a structured approach towards guiding readers from traditional IR models to advanced, hybrid frameworks. The early chapters focus on classical and modern retrieval techniques with comparative analyses of different methods. Subsequent chapters focus on applied scenarios such as tourism recommender systems, sentiment mining from YouTube comments, book and medicine recommendation engines, and image-audio-based retrieval systems. Advanced topics include semantic role classification using BERT, hybrid filtering methods, personalised web crawlers, and experimental studies on smoothing techniques. Real-world case studies and experimental evaluations illustrate how theoretical models translate into effective, domain-specific IR applications.Key FeaturesComprehensive coverage of traditional, modern, and hybrid IR techniquesPractical frameworks for recommendation systems, sentiment analysis, and web crawlingIntegration of AI and machine learning methods, including BERT and TF-IDF modelsExperimental evaluations and comparative analyses across multiple domainsReal-world applications spanning tourism, healthcare, fashion, and multimedia retrieval
Information Retrieval (IR) techniques are growing continuously from being keyword-based systems to advanced search. These days, IR techniques utilize Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) for providing more accurate and personalized results. In the proposed research work, the IR techniques are analysed for their merits and demerits. In the work, it has been examined how contemporary research has been transformed into query document matching. This work integrates Term Frequency-Inverse Document Frequency (TF-IDF) into two retrieval metrics—cosine similarity and dot product similarity. Integration aims to provide better results. Cosine similarity is good at capturing vector orientation, while dot product similarity is good for vector magnitude. A combined similarity is weighted at parameter α to enhance the retrieval capacity. From the simulation of work, it has been calculated that the combined method performed well. In the future, authors will incorporate machine learning or deep learning methods to enhance the performance of these IR techniques.
As digital information is growing day by day, IR techniques need to be more accurate so that the required information can be retrieved on time. To improve the IR system, the authors analyzed different IR techniques to find the merits and demerits of the existing methods. There is a significant improvement in IR techniques if we consider the growth from traditional techniques to modern techniques. Modern techniques can handle diverse data and retrieve accurate results on time [16]. Due to the exponential growth of digital data, the components of search range from educational content to social media, transport, e-commerce, healthcare, and many more.
The user experience is improved by maintaining scalability and confirming the relevance of the content [17].Fig. (1) represents some measure functions that are
required to be performed before the process of actual search starts, such as understanding how to formulate the query using some special keywords like OR, AND, NOT,etc. [18]. First, we need to understand the classical methods and then apply the modern methods for information retrieval. The data needs to be stored in a structured way for efficient query retrieval. The text pre-processing includes tokenization, stop-word removal, stemming, and much more needs to be done. Users are also required to capture the semantic relationship in data. The dimensions of data are also required to be reduced so that hidden relationships can be captured on time.Fig. (2) shows the different components of the IR system.
The paper is organized into a total of 5 sections. Section 1 is about the introduction of IR. The general prior steps, along with the components of IR, are explained. Section 2 discusses the literature review with the help of a literature summary table. Section 3 i
