Introduction
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 Features
- - Comprehensive coverage of traditional, modern, and hybrid IR techniques.
- - Practical frameworks for recommendation systems, sentiment analysis, and web crawling.
- - Integration of AI and machine learning methods, including BERT and TF-IDF models.
- - Experimental evaluations and comparative analyses across multiple domains.
- - Real-world applications spanning tourism, healthcare, fashion, and multimedia retrieval.
Target Readership:
Graduate students, researchers, academics and professionals in computer science, information retrieval, data science, AI, and machine learning.