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Logo: Institut für Verteilte Systeme - Fachgebiet Wissensbasierte Systeme (KBS)
Logo Leibniz Universität Hannover
Logo: Institut für Verteilte Systeme - Fachgebiet Wissensbasierte Systeme (KBS)
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Important Infos

Course start: 09.04.2013, 15.00-16:30, Multimedia-Hörsaal (3703 - 023)


Teaching Team

  • Responsible Professor: Prof. Dr. techn. Wolfgang Nejdl
  • Assistance: Sergej Zerr  (Home Page), Xiaofei Zhu  (Homepage)
  • NOTE: Please put "WebScienceCourse" into the subject line when writing an email


  • Lecture: Tuesdays 15:00 - 16:30
  • Room: Multimedia-Hörsaal (3703 - 023), Appelstraße 4, 30167 Hannover
  • Live - Transmition:  https://webconf.vc.dfn.de/websciencecourse (please ask Sergej or Xiaofei for the password to access this virtual room)

Oral Exam

The oral exam is composed of two parts:

  1. Detailed questions on the papers presented by the student during the course. The presentation of a paper is compulsory!
  2. More general questions on other papers of the same topicand some on other topics. As a guideline you should be able to answer the following questions:
  • What is the problem addressed in the paper?
  • How does the solution look like?
  • How is it evaluated?

    Send an email to Sergej, or Xiaofei to book a time slot for the oral exam. Please notify if the date is not appropriate anymore for you!

    2. OG, Appelstraße 4, 30167 Hannover. Please ring the bell to enter, and wait in the waiting room in the middle of the hall.

    Slide Template
    Please use the provided slide template for your presentation. (powerpoint latex)

Topics for Student Paper Presentation

Below are the topics of Web Science which will be addressed in the course. Each student will have to pick two papers that she/he will present to the other students in the second part of the course. Details about how to subscribe will follow.


Here we collected hints helping you to prepare a good presentation.

Here a standard PowerPoint template for the presentation, here the Latex version. This template is not mandatory! Please feel free to use your own designs. Here is a good example for a student work from last year.


Send a mail to Sergej or Xiaofei with the following details:

  • At least 2 papers that you wish to present.
  • If there is a laps of time during the semester lecture period when you absolutely cannot present, please mention this in the mail.
  • If there is a period when you would preferably present, please also mention this.

Note that we try to take into account the following criteria when attributing the one paper which students will present:

  • Papers will be assigned to students as soon as possible according to the first come first served policy.
  • The exact presentation date will be definitely fixed as soon as 2 papers about a same topic has been attributed.
  • Presentations about a same topic should take place the same day.
  • As far as possible a similar number of paper per topic would be presented.
  • Each topic should at least have one paper presented.

Available Topic Papers

Below are papers available to you for presenting, grouped by topics. Papers already attributed to a student are marked as such. Papers from topics of which two papers are already attributed are no more available for presentation. 

1. Learning to Rank for Information Retrieval

  • Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '02). (Selected by Japreet) pdf
  • D. Sculley, “Large Scale Learning to Rank”. NIPS 2009 Workshop on Advances in Ranking. December, 2009.(Selected by Japreet) pdf
  • Hang Li, “A Short Introduction to Learning to Rank”, IEICE Transactions on Information and Systems, E94-D(10), 2011.pdf
  • Hang Li, “Tutorial on Learning to Rank”. Microsoft Research Asia. presentation pdf

2. Recommender Systems

  • Koren, Y.; Bell, R.; Volinsky, C. “Matrix Factorization Techniques for Recommender Systems”. IEEE Computer, Volume 42, Issue 8, p.30-37 (2009). (Selected by Marcus) pdf
  • Ernesto Diaz-Aviles, Lucas Drumond, Zeno Gantner, Lars Schmidt-Thieme, and Wolfgang Nejdl. 2012. What is happening right now … that interests me?: online topic discovery and recommendation in twitter. In Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM '12). pdf
  • Mukund Deshpande and George Karypis. 2004. “Item-based top-N recommendation algorithms”. ACM Trans. Inf. Syst. 22, 1 (January 2004), 143-177.pdf
  • Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing 7, 1 (January 2003).   (Selected by Marcus) pdf

3. Collaborative Web

  • Morris MR, Horvitz E. SearchTogether: an interface for collaborative web search. Symposium on User Interface Software and Technology. 2007:3-12. (Selected by Florian) ACM Link
  • Abel F, Herder E, Marenzi I, Nejdl W, Zerr S. Evaluating the Benefits of Social Annotation for Collaborative Search. Workshop on Search in Social Media, co-located with ACM SIGIR Conference on Information Retrieval, Boston, MA, USA. 2009. pdf
  • Morris MR. A survey of collaborative web search practices. Conference on Human Factors in Computing Systems. 2008:1657-1660. (Selected by Benjamin) pdf
  • Agrahri AK, Manickam DAT, Riedl J. Can people collaborate to improve the relevance of search results? ACM Conference On Recommender Systems. 2008:283-286. (Selected by Florian)
  • Boydell O, Smyth B. Social summarization in collaborative web search. Information Processing & Management. 2010;46(6):782-798. (Selected by Benjamin) online pdf

4. Query Recommendation

  • S. Bhatia, D. Majumdar, and P. Mitra. Query suggestions in the absence of query logs. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR'11), 2011:795-804. (Selected by Muhammad) pdf
  • Y. Liu, R. Song, Y. Chen, J. Nie, and J. Wen. Adaptive query suggestion for difficult queries. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (SIGIR'12), 2012:15-24. (Selected by Ingo) ACM link
  • R. Li, B. Kao, B. Bi, R. Cheng, and E. Lo. DQR: a probabilistic approach to diversified query recommendation. Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM'12), 2012:16-25. (Selected by Muhammad) ACM link
  • Y. Song, D. Zhou, and L. He. Query suggestion by constructing term-transition graphs. Proceedings of the fifth ACM international conference on Web search and data mining (WSDM'12), 2012:353-362. (Selected by Ingo) pdf

5. Mining the Social Web: Case Studies from Twitter

  • M.D. Conover, J. Ratkiewicz, M. Francisco, B. Goncalves, A. Flammini, and F. Menczer. Political polarization on twitter. In Proc. 5th Intl. Conference on Weblogs and Social Media, 2011. pdf
  • Ricardo Kawase, Bernardo Pereira Nunes, Eelco Herder, Wolfgang Nejdl, and Marco Antonio Casanova. Who wants to get fired? CHI, April 27- May 2 2013.  (Selected by Prabhakar) pdf
  • Marco Pennacchiotti and Ana-Maria Popescu. Democrats, republicans and starbucks affcionados: user classification in twitter. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pages 430-438, New York, NY, USA, 2011. ACM. pdf
  • Ingmar Weber, VenkataRamaKiran Garimella, and Asmelash Teka. Political hashtag trends. In Advances in Information Retrieval, volume 7814 of Lecture Notes in Computer Science, pages 857-860. Springer Berlin Heidelberg, 2013. (Selected by Prabhakar) Springer link

6. Semantic Web 

  • M. Greaves, P. Mika. Semantic Web and Web 2.0. Web Semantics Sci Serv Agents World Wide Web (2008).  pdf
  • Franz Baader, Ian Horrocks, and Ulrike Sattler. Description Logics as Ontology Languages for the Semantic Web. In Festschrift in honor of Jörg Siekmann, Lecture Notes in Artificial Intelligence (2003), pp. 228-248 pdf
  • Peter Dolog and Nicola Henze and Wolfgang Nejdl and Michael Sintek. Towards the Adaptive Semantic Web. Principles and Practice of Semantic Web Reasoning (2003), pp. 51-68.(Selected by Martin Liro) pdf
  • Peter Dolog and Wolfgang Nejdl. Semantic Web Technologies for the Adaptive Web. The Adaptive Web (2007), pp. 697-719.(Selected by Martin Liro)  pdf 

Detailed Schedule


Introduction to Web Sciences

  • Course Overview & Organizational Issues (Wolfgang Nejdl)
  • Slides PPT 
  • Introduction to Web Sciences (Wolfgang Nejdl)
  • Slides  PPT 



  • Introduction to Web Sciences (Wolfgang Nejdl)
  • Slides PPT



  • Collaborative Web (Sergej)
  • Slides PPT | PDF



  • Mining the Social Web (Asmelash)
  • Slides  KEY PDF



  • Semantic Web & Ontologies (Helmi)
  • Slides  KEY PDF



  • Query Recommendation (Xiaofei)
  • Slides   PPT



  • Learning to Rank for Information Retrieval(Ernesto)
  • Slides KEY | PDF



  • Recommender Systems (Ernesto)
  • Slides KEY | PDF



  • Topic: Collaborative Web
  • Presenter 1: Florian 
  • Slides PDF1 PDF2
  • Presenter 2: Benjamin
  • Slides PDF
  • Topic: Learning to Rank for Information Retrieval
  • Presenter 3: Japreet
  • Slides PDF



  • An Introduction to Web Retrieval (online)
  • Link



  • Social Media Analytics (online)
  • Link



  • Topic: Query Recommendation 
  • Presenter 1: Muhammad 
  • Slides PPT
  • Topic: Semantic Web & Ontologies
  • Presenter 2: Stoney
  • Slides PPT



  • Topic: Mining the Social Web
  • Presenter 1: Prabhakar
  • Slides PDF
  • Topic: Query Recommendation
  • Presenter 2: Helge Holzmann
  • Slides PDF

Final Oral Exam


  • Di 23. Jul. 2013            11:30  13:00
  • Student 1: Jaspreet Singh
  • Di 13. Aug. 2013          10:00  12:00 
  • Student 1: Helge Holzmann
  • Di 27. Aug. 2013          10:00  12:00 
  • Student 1: Stoney
  • Mi 11. Sep. 2013          10:00  12:00 
  • Student 1: Sharma Dahal Prabhakar
  • Student 2: Martin Liro
  • Student 3: Muhammad Nuruddin

(Notes: Please select one of the dates by sending an email to Xiaofei Zhu(zhu@l3s.de) or Sergej Zerr (zerr@l3s.de ) )