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 Library and Information Science students with comprehensive knowledge of modern Information Retrieval (IR) systems, from fundamental concepts to cutting-edge applications. The curriculum covers traditional IR foundations like Boolean and vector space models, advances to practical applications in academic databases (Scopus/PubMed), and culminates in emerging technologies like neural IR and LLMs. Special emphasis is placed on metadata management, query processing, user behavior analysis, and the integration of AI technologies. The course balances theoretical understanding with practical applications in library and scholarly contexts, preparing students for the evolving landscape of digital 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.

Assignments and Course Requirements: Assignments play a critical role in building your understanding of information retrieval concepts and methods. Each assignment gives you hands-on experience with different IR techniques, search strategies, and evaluation methods. Late submissions will affect your grade, so please manage your time carefully and start working on assignments early. Detailed instructions for each assignment will be provided in class and posted on the course website. Exams Exams will be multiple choice format and strictly closed-book - no textbooks, notes, computers, phones, or other materials are allowed. The questions will test your understanding of core IR concepts and methods through carefully designed multiple choice options. Handling Absences Due to Early Employment or Internship: If students anticipate being absent for an extended period due to early employment or internship opportunities, it is essential that they inform the instructor in advance. This proactive communication helps to minimize any potential negative impact on their academic progress and ensures appropriate arrangements can be made to support their learning.

This course does not require any prerequisites, making it accessible to all students interested in learning the fundamentals of information retrieval (IR) and modern search techniques.

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.