Information Retrieval
Course infoSchedule
Course moduleNWI-I00041
Credits (ECTS)6
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; Informatica en Informatiekunde;
prof. dr. ir. A.P. de Vries
Other course modules lecturer
prof. dr. ir. A.P. de Vries
Other course modules lecturer
Contactperson for the course
prof. dr. ir. A.P. de Vries
Other course modules lecturer
prof. dr. ir. A.P. de Vries
Other course modules lecturer
Academic year2018
KW1-KW2  (03/09/2018 to 27/01/2019)
Starting block
Course mode
RemarksStudents for whom the course is compulsory in their programme have first access.
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedureIn order of Study programme
ExplanationIn order of Study programme
The objective is that participants in the course
  1. are familiar with the classic retrieval models
  2. understand the limitations and assumptions associated with these models
  3. have insight and proficiency in the design and construction of search engines
  4. are familiar with the standard evaluation methods for IR systems
  5. are familiar with interaction techniques to support searchers in their quest for information
  6. have an understanding of how the searcher's context and behaviour can be used to enhance retrieval effectiveness
  7. have gained familiarity with recent scientific literature in this field
While the rise of the internet has helped strengthen the field of Information Retrieval (IR), the area stretches far beyond plain web search, as a discipline situated between information science and computer science. In 1968, Gerard Salton defined information retrieval as "a field concerned with the structure, analysis, organization, storage, searching, and retrieval of information". Even though the area has seen many changes since that time and made a tremendous impact (who has never used a search egine?!), that definition is still accurate.
IR takes the notion of "relevance" as its core concept. As the scope of IR is limited to those cases where computers try to identify the relevance of information objects given a user's information need (as opposed to humans doing that, the common scenario in information science), perhaps "Computational Relevance" would have been a better term for the research in this area.
In this course, we cover the following aspects of Information Retrieval:
  1. How do people search for information, and how can this be formalized?
  2. How can we take advantage of term statistics, structure and annotations to capture the meaning of texts?
  3. How can these elements be combined in order to find "relevant" information?
  4. What techniques are necessary to scale to large text collections?
Additional comments
This year, the course will be reformed (and differ from the information retrieval course in previous years).

The course consists of two main parts:

• Fundamentals
• The term vocabulary and postings lists, inverted files
• Stemming, normalization
• Scoring, term weighting and the vector space model
• Statistical language models and their application to IR
• Evaluation
• Relevance feedback and query expansion
• Exploration of IR application areas
• Documents and structure
• Document Classification
• Pagerank/ anchors
• Social media and IR / click data
• Recommender systems
• User interaction aspectsGuest speakers may be invited to discuss state-of-the-art topics.

Test information
Written exam (divided in two parts, a mid-term and a final test) in addition to seminar presentations and practical work.

Participant of Information Retrieval should have the base qualifications as provided by the bachelor Computing Science, Information Science or Artificial Intelligence.

Required materials
C.C. Manning, P. Raghavan, H. Schutze, Introduction to Information Retrieval,Editor: Cambridge. Available online at http://nlp.stanford.edu/IR-book/
W. Bruce Croft, Donald Metzler, Trevor Strohman. Information Retrieval in practice, Editor: Pearson. See also: http://www.search-engines-book.com/ Online version from CIIR: SEIRiP.pdf
Lecture notes will be made available via Brightspace.

Recommended materials
Ryen W. White, Interactions with Search Systems, Editor: Cambridge University Press. http://www.cambridge.org/9781107034228

Instructional modes
Course occurrence



Students work in teams and evaluate an IR approach of their choosing.

Self study

Test 1
Test weight4
Test typeTest
OpportunitiesBlock KW2, Block KW3

Test 2
Test weight3
Test typeTest
OpportunitiesBlock KW2, Block KW3

Test weight3
Test typeAssignment
OpportunitiesBlock KW2, Block KW3