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CS 242: Information Retrieval and Web Search

Search engines are becoming more important as the amount of available data increases. Information Retrieval (IR) studies the theoretical and practical issues of designing and building search engines. This course will expose students to the challenges and solutions of designing IR algorithms, evaluating them and building them. Some of the topics which will be presented are: Vector Space Model, Probabilistic IR, Web crawling, text processing, IR evaluation methods, Web search, IR-style search of structured and semi-structured data, personalized search, search in social networks and scalable implementations. The course will also cover advanced IR topics from research publications. By the end of this course students will be able to write algorithms to perform IR and search the web, as well as developing custom-made search engines.

  • About the Instuctor
    Vagelis Hristidis

    Vagelis Hristidis (aka Evangelos Christidis) received his Ph.D in Computer Science from University of California, San Diego in 2004. His key areas of expertise are Databases, Information Retrieval, and particularly the intersection of these two areas. His long-term vision is to make the information in databases easily accessible and useful, in various application domains. His research has strong emphasis on interdisciplinary topics, mainly Health Informatics (where he has published a book) and Disaster Management. His key achievements include the NSF CAREER award, the Google Research Award, the IBM Scalable Data Analytics for A Smarter Planet Innovation Award, and the Kauffmann Entrepreneurship Award.