INFORMATION RETRIEVAL

문헌정보학 중에서 특히 정보학영역의 핵심을 이루는 과목으로서, 정보의 축적과 검색을 함께 다룬다. 파일구조의 수학적 모델링과 탐색기술을 포함하여 정보검색의 이론적 토대를 공부하며, 다양한 학문분야로부터 채택된 커뮤니케이션 모델도 함께 연구한다.

This course aims to provide a comprehensive understanding of modern Information Retrieval systems, spanning foundational concepts and recent technological developments in digital information environments. Specifically, it aims to: * explain core Information Retrieval models and principles, including Boolean, vector space, and probabilistic approaches; * examine the structure and functionality of major academic databases and scholarly search systems; * analyze the roles of metadata, indexing, and query processing in retrieval effectiveness; * evaluate user behavior and interaction patterns in contemporary search environments; * assess the impact of neural Information Retrieval and large language models on current and future information services.

Use of English: This course will be conducted in English, and students are expected to use English whenever possible, including for homework assignments, presentations, and other course-related activities. Attendance and Classroom Behavior: Class attendance and active participation are essential for success in this course. Arriving late or frequently stepping out of class is inappropriate, as it can disrupt the learning environment and hinder both individual and group progress. If you anticipate any issues with attendance or participation, or if unexpected challenges arise during the semester, please contact me as soon as possible so we can discuss appropriate accommodations or solutions.

This course does not require any prerequisites.

Information Retrieval (IR) Theory focuses on the principles and technologies that enhance the efficiency of searching and extracting information. This field emphasizes the development of modern search systems, particularly those based on artificial intelligence (AI) and machine learning (ML). Practical applications include advanced search techniques in academic databases like Scopus and PubMed, multimedia search systems, and personalized search results that adapt to user behavior. Additionally, recent advancements in AI and natural language processing (NLP) have led to improvements in query refinement and cross-language search optimization. These innovations have significantly enhanced the accuracy, efficiency, and user-centered design of information retrieval systems.