Basic and advanced technologies for information retrieval, indexing and ranking; indexing of non-textual data; boolean models and vector space models for search; evaluation and user interface issues; the structure of Internet search engines.
DD2477 Search Engines and Information Retrieval Systems 7.5 credits
The course discusses methods for indexing, ranking and searching in large document collections, as well as methods for evaluating different search models.
Information per course offering
Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.
Information for Spring 2026 Start 13 Jan 2026 programme students
- Course location
KTH Campus
- Duration
- 13 Jan 2026 - 1 Jun 2026
- Periods
- P3 (4.5 hp), P4 (3.0 hp)
- Pace of study
25%
- Application code
60251
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
Open to all programmes from year 3 and for students admitted to a master's programme as long at is can be included in your programme.
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
Master's Programme, Computer Science, åk 1, CSCS, Recommended
Master's Programme, Computer Science, åk 1, CSDA, Conditionally Elective
Master's Programme, Computer Science, åk 1, CSST, Recommended
Master's Programme, Computer Science, åk 2, CSCS, Recommended
Master's Programme, Computer Science, åk 2, CSST, Recommended
Master's Programme, ICT Innovation, åk 1, DASC, Recommended
Master's Programme, ICT Innovation, åk 1, DASE, Recommended
Master's Programme, ICT Innovation, åk 1, VCCN, Recommended
Master's Programme, ICT Innovation, åk 2, DASC, Recommended
Master's Programme, ICT Innovation, åk 2, DASE, Recommended
Master's Programme, Industrial Engineering and Management, åk 1, MAIG, Conditionally Elective
Master's Programme, Industrial Engineering and Management, åk 1, PUET, Conditionally Elective
Master's Programme, Machine Learning, åk 1, Conditionally Elective
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus DD2477 (Autumn 2025–)Headings with content from the Course syllabus DD2477 (Autumn 2025–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
After passing the course, the student shall be able to
- explain and use concepts in information retrieval such as indexing, ranking, boolean search model, vector space model
- implement methods for indexing, search, and ranking of a very large number of documents with hyperlinks
- use tools and software libraries for information retrieval
- be able to evaluate algorithms and systems for information retrieval
in order to be able to
- work for companies that specialise in information retrieval
- carry out a master's degree project in computer science with a specialisation in information retrieval
- be an important link between systems designers, programmers, and interaction designers in industry as well as in research projects.
Literature and preparations
Specific prerequisites
Knowledge and skills in programming, 6 credits, equivalent to completed course DD1310-DD1319/DD1331/DD1333/DD1337/DD1321/DD100N/ID1018.
Knowledge of algorithms and data structures, 6 credits, equivalent to completed course DD1320-DD1328/DD1338/ID1020/ID1021.
Knowledge of linear algebra, 7.5 credits, equivalent to completed course SF1624/SF1684/SF1672.
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
A, B, C, D, E, FX, F
Examination
- LAB1 - Computer assignments, 4.5 credits, grading scale: A, B, C, D, E, FX, F
- PRO1 - Project assignment, 3.0 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
Computer Science and Engineering
Education cycle
Second cycle
Supplementary information
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex
DD2477 is replacing and overlapping DD2476 and therefore cannot be combined with that course.