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.

  • Educational level

    Second cycle
  • Academic level (A-D)

  • Subject area

    Computer Science and Engineering
  • Grade scale

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

Course offerings

Spring 17 ir17 for programme students

Spring 17 SAP for single courses students

  • Periods

    Spring 17 P3 (6.0 credits), P4 (3.0 credits)

  • Application code


  • Start date

    2017 week: 3

  • End date

    2017 week: 23

  • Language of instruction


  • Campus

    KTH Campus

  • Number of lectures

    12 (preliminary)

  • Number of exercises

  • Tutoring time


  • Form of study


  • Number of places

    No limitation

  • Schedule

    Schedule (new window)

  • Course responsible

    Johan Boye <jboye@kth.se>

  • Teacher

    Jussi Karlgren <jussi@kth.se>

    Viggo Kann <viggo@kth.se>

  • Target group

    Single course 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, SF1625 Calculus in one variable, SF1626 Calculus in several variables, SF1901 Probability theory and statistics, DD1337 Programming, DD1338 Algorithms and Data Structures, DD2431 Machine Learning, DD2380 Artificial Intelligence, DD1364 Database Technology or corresponding courses.

Recommended prerequisites

A level in Mathematics corresponding to at least 30 credits, including courses in Linear Algebra, Calculus in one and several variables, Mathematical Statistics, and a level in Computer Science corresponding to at least 15 credits. It is also beneficial to have taken courses in Machine Learning, Artificial intelligence, Language Engineering and/or Database Technology.


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


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

CSC/Speech, Music and Hearing


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


Johan Boye <jboye@kth.se>


Course syllabus valid from: Autumn 2016.
Examination information valid from: Spring 2013.