A Comprehensive Study of Case-Based Reasoning in Information Retrieval: An Artificial Intelligence Approach

Information Retrieval (IR) systems which provide accurate and contextually relevant information to healthcare and law etc. where accuracy is very crucial. Traditional information retrieval methods fundamentally based on keyword matching and statistical models usually difficult to achieve the semantic meaning and contextual nuances of queries. Case-Based Reasoning (CBR), as an artificial intelligence technique, provide advance solutions to enhance IR systems based on a set of past cases through adaptive context-supporting retrieval. By taking advantage of past cases data, CBR allows IR systems to interpret complex context-sensitive queries and offer personalized results. The paper provides an overview of traditional IR models, examines the CBR integration into these systems, and evaluates the impact of CBR on retrieval effectiveness and personalization. And finally, we elaborate on the challenges involved with scaling and adapting cases towards real-time retrieval and provide some future directions for research, including integrating machine learning techniques and hybrid models to enhance the relevance, interpretability, and scalability of CBR-based IR systems.