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Web Information Retrieval

Information Retrieval (IR) is dealing with the storage, representation and management of information items. In a classical setting the information items correspond to text documents. With the advent of the World Wide Web, the methods of IR have been transferred to retrieval on the web. This poses different challenges and has spawned the area of Web Retrieval.

The lecture will give an introduction in established retrieval models for text based documents, models that exploit the graph structure of the WWW, the topic of evaluating the performance of retrieval systems and related tasks like classification and clustering of web documents.

Web Information Retrieval (6 ECTS-Credits) is a lecture given in English that

  • is a mandatory course for master students of Web Science
  • can be taken as an elective course by bachelor and master students of Informatik and Computervisualistik, and by master students of Wirtschaftsinformatik and Information Management

News

  • The lectures will start April 24, 18:00. The first tutorials will take place on May 2, 12:00 and May 5, 10:00.

Organizational Information

Lecture (Klips)

  • Lecturer: Dr. Chandan Kumar
  • Mondays, 18:00 - 20:00, E 313

Tutorial (Klips)

  • Instructor: Lukas Schmelzeisen (substitute for René Pickhardt)
  • Tuedays, 12:00 - 14:00, C 206
  • Fridays, 10:00 - 12:00, E 114
  • You don't need to come to both slots for the tutorial. We will cover the same material Tuesdays and Fridays.

Material

Slides and additional material will be provided along with the progress of the lecture. 

Lecture Topics

  1. Organization (PDF)
  2. Introduction (PPT) (PDF)
  3. Evaluation (PPT) (PDF)
  4. Boolean Model  (PPT) (PDF)
  5. Vector Space Model  (PPT) (PDF)
  6. Probabilistic Language Model (PPT) (PDF)
  7. Probabilistic Retrieval (PPT) (PDF)
  8. Web Search Characteristics  (PPT) (PDF)
  9. Web Crawling (PPT) (PDF)
  10. Authority Ranking - PageRank (PPT) (PDF)
  11. User interfaces, Visualizations, Eyetracking (PPT) (PDF)
  12. Multimedia Retrieval (PDF)

It is highly recommended that you follow a textbook while taking the lecture. The textbooks are probably able to address most question you might have about the content of the lecture:

  • Introduction to Information Retrieval. Manning, Raghavan, Schütze, Cambridge University Press, 2008.
    Free, electronic versions available at http://informationretrieval.org/
  • Web Data Mining. Liu. Springer, 2007.
  • Modern Information Retrieval. Baeza-Yates, Ribeiro-Neto, ACM Press, 2012.

If you ever have any questions or want to discuss something about the lecture or the assignments, feel free to ask

  • your colleagues
  • in the Web Science newsgroup infko.webscience.
  • in our Facebook group (Don't worry if you don't have/want Facebook, joining this group is by no means required, all news will be published here and in the newsgroup.)
  • the teachers via e-mail

Tutorials / Assignments

You should complete the assignments in groups of 4 people. Please register into a group until Tuesday, May 2, 2017 at https://ist.uni-koblenz.de/teams/en/user/registration/5471b0f2-57bd-4712-bedf-b6023141d542





Assignment Submission until Solution Tutorial slides
Tutorial Organization
Assignment 1
(iter_corpus_docs.py)
May 9, 2017, 10:00 a.m. Solution 1 Tutorial 1
Assignment 2 May 16, 2017, 10:00 a.m. Solution 2 Tutorial 2
Assignment 3 May 23, 2017, 10:00 a.m. Solution 3 Tutorial 3
Assignment 4 May 30, 2017, 10:00 a.m. Solution 4 Tutorial 4
Assignment 5 June 13, 2017, 10:00 a.m. Solution 5 Tutorial 5
Assignment 6 June 27, 2017, 10:00 a.m. Solution 6 Tutorial 6
Assignment 7 July 4, 2017, 10:00 a.m. Solution 7 Tutorial 7
Assignment 8 July 11, 2017, 10:00 a.m. Solution 8 Tutorial 8
Repetition Tutorial

Exam

In order to obtain ECTS-Credits (6 ECTS-Credits) you need to both gain admission to the exam and you need to pass the exam. The exam is passed if you obtain a score of at least 50% in it.

Only students who have gained admission are allowed to participate in the exam. Admission is reached by obtaining a total score of at least 50% over all excercise assignments. Admissions from previous semesters are not recognized, with the only exception that you failed the exam in SS 2016 and are thus required to take it again. Nevertheless, participation in the lecture and exercise is strongly recommended by us.

For your reference, the exam from year 2015 can be found here.

Beteiligte: 

Dr. Chandan Kumar

kumar@uni-koblenz.de

Lukas Schmelzeisen

lukas@uni-koblenz.de

René Pickhardt

rpickhardt@uni-koblenz.de