DD2476 Search Engines and Information Retrieval Systems 9.0 credits

Sökmotorer och informationssökningssystem

A course in computer science focusing on basic theory, models, and methods for information retrieval.

  • Education cycle

    Second cycle
  • Main field of study

    Computer Science and Engineering
  • Grading scale

    A, B, C, D, E, FX, F

Course offerings

Spring 19 ir18 for programme students

Spring 19 ir19 for Study Abroad Programme (SAP)

  • Periods

    Spring 19 P3 (6.0 credits), P4 (3.0 credits)

  • Application code


  • Start date


  • End date


  • Language of instruction


  • Campus

    KTH Campus

  • Tutoring time


  • Form of study


  • Number of places

    No limitation

  • Schedule

    Schedule (new window)

  • Course responsible

    Johan Boye <jboye@kth.se>

  • Teacher

    Viggo Kann <viggo@kth.se>

  • Target group

    Only open för students within Study Abroad Programme (SAP)

Spring 20 ir20 for programme students

Intended learning outcomes

After completing the course you will be able to:

*  explain the concepts of indexing, vocabulary, normalization and dictionary in Information Retrieval,

*  give an account of different text similarity measures, and select a similarity measure suitable for the problem at hand,

*  define a boolean model and a vector space model, and explain the differences between them,

*  implement a method for ranked retrieval of a very large number of documents with hyperlinks between them,

*  evaluate information retrieval algorithms, and give an account of the difficulties of evaluation,

*  give an account of the structure of a Web search engine.

Course main content

Basic and advanced techniques for information systems: information extraction; efficient text indexing; indexing of non-text data; Boolean and vector space retrieval models; evaluation and interface issues; structure of Web search engines.


SF1604 Linear algebra,  SF1901 Probability theory and statistics,  DD1338 Algorithms and Data Structures, or corresponding courses.

Recommended prerequisites

A level in Mathematics corresponding to at least 30 credits, including courses in Linear Algebra and Mathematical Statistics, and a level in Computer Science corresponding to at least 15 credits.


C. D. Manning, P. Raghavan and H. Schütze: Introduction to Information Retrieval, Cambridge University Press, 2008.


  • LABA - Laboratory lessons, 6.0, grading scale: A, B, C, D, E, FX, F
  • LABB - Laboratory lessons, 3.0, grading scale: A, B, C, D, E, FX, F

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.

Offered by

EECS/Intelligent Systems


Johan Boye, e-post: jboye@kth.se


Johan Boye <jboye@kth.se>


Course syllabus valid from: Spring 2019.
Examination information valid from: Spring 2019.