880488 :Information Search (HAIT/DJ)

General info

Instruction language English
Type of Instruction 12 x 2 hours lectures (No data available yet)
Type of exams Assignments and written exam (Examination schedule)
Course load:6 ECTS credits
Registration:Enroll via COMAP. Enrolment from October 1 to October 15
Blackboard Infonot available in Blackboard


dr. S. Wubben

dr. M.M. van Zaanen


At the end of the unit, students will be able to

  • describe the components of information retrieval systems, including the ordering, storage and searching of the data, explain how they can be implemented, and perform the internal  computations;
  • describe search systems that focus on web search, experts and expertise, answering questions, social information (such as social tagging), and different modalities (such as images and music) and analyze the interaction between their components;
  • describe, apply and compare current approaches to the evaluation of the information retrieval tasks discussed during the lectures (including the corresponding evaluation measures) and identify pros and cons of each approach.


Search engines are a crucial part of the world wide web. These search engines make content accessible and satisfy the information needs of millions of users every day by matching these needs to documents, videos, social media and other types of information available. It is no surprise that search engines have become big business and have spawned all kinds of marketing and advertising industries. 

Recommender systems take this search process one step further by actively recommending interesting items to people. They guide people based on information from other people. The information that other people provide may come from explicit ratings, tags, or reviews, or implicitly from how they spend their time or money. The information can be aggregated and used to select, filter, or sort items and the recommendations may be personalized to the preferences of different users. Companies such as Amazon, Netflix and other web stores make extensive use of recommender systems to sell items to their customers.

Another development is the increasing popularity of question answering systems, such as Apple's Siri, that deliver answers and services based on user questions in natural language. This enables easier use of search and recommendation technologies on for instance mobile devices.

In this course, students will become familiar with the workings of information retrieval, search engines, recommender systems, question answering systems and other applications of this technology. 

Topics treated:

  • information retrieval models
  • search engines
  • web indexing
  • multimedia retrieval & other retrieval tasks
  • question-answering systems
  • recommender systems
  • social tagging
  • evaluation of information retrieval



The final grade is calculated based on the grade for the written exam (80%) and two individual assignments (10% each). Assignments have non-negotiable deadlines. Assignments handed in after the deadline will not be accepted and will lead to a fail for the course.

Compulsory Reading

  1. Christopher D. Manning, Prabhakar Raghavan, Hinrich Sch├╝tze, Introduction to Information Retrieval, Cambridge University Press, 2008, ISBN 978-0-521-86571-5.