Web Retrieval[go to overview]
Results of the oral exam are now available online (PDF)!
Information Retrieval refers to methods and technologies for search, analysis, and automatic organization of data collections: text documents, multimedia contents, structured and semi-structured knowledge representations. It has quickly become one of the most important areas in Computer and Information Sciences because of its direct applications in e-commerce, e-CRM, corporate knowledge bases and data repositories, Web analytics, and Web information systems. Recent technological and research trends, such as Linked Open Data and Web Science, are closely related to Information Retrieval and offer new perspectives for data/knowledge organisation and search. The course will introduce mathematical models and algorithms widely used by Web search engines, intranets, and modern digital libratries. In doing so, we will consider state of the art techniques from linear algebra, statistics, graph mining and machine learning. The course will also provide a brief overview of other areas in Web mining, such as Web content mining and Web structure mining.
Lecturer: Dr. Dr. Sergej Sizov
The lecture is given in English. Lectures (corresponding to 2h/week).
Credits: 2 SWS, 3 ECTS
This lecture is part of the international summer academy and therefore is held in the last 4 weeks of the term. Students from Koblenz may however attend and earn credits as usual.
Deeper understanding of state of the art search/retrieval systems and algorithms, their limitations and recent research/development challenges; ability to design and to improve Information Retrieval systems.
Basic knowledge in linear algebra, stochastics and graph algorithms. Recommended prerequisites are courses in data mining and database systems.
Individual oral exam (30 min) at the end of the course.
Here is the corresponding entry for the lecture in Klips.
Course topics include technical basics (linear algebra, stochastics, graph algorithms, text processing), an overview of common IR methods and models (vector space models, link analysis and authority ranking, multimedia retrieval, organization and ranking of search results) as well as advanced IR topics, such as top-k retrieval, focused crawling, multi-modal analysis of Social Web, and distributed IR.