Prof. Dr. Felix Naumann

Recommender Systems

Lecturer: Dr. Ralf Krestel


Recommender systems have become very popular in recent years. Many applications include recommender algorithms in one way or another. An early example of industrial application of recommender systems is recommending books by Amazon. Other application areas include movies, music, news, web queries, tags, and products in general. Independent of the application domain, various approaches have been developed to improve recommendations. Further, explanations of recommendations and evaluating recommender systems are active research fields, together with psychological and economical implications, as well as privacy concerns. We will look at different aspects of recommender systems and fundamental algorithms of the field.

In this seminar, each student will present one topic related to recommender systems in a 30-minutes talk followed by 15 minutes of discussion. At the beginning of the semester each student should present his/her topic in a 5-minutes short talk. At the end of the semester, each student has to hand in a written summary report (5 pages; two column style) of his/her topic. Active participation in all discussions is mandatory.

This seminar is limited to 6 participants. If more apply we will pick randomly.

The grade will consist of

  • 30% Presentation
  • 30% Active Participation
  • 40% Summary Report

The seminar takes place on Monday at 13:30 in A-2.2.


Date |Topic | Presenter    
13.10.14|Introduction | Dr. Ralf Krestel    
17.10.14|Application Deadline |    
18.10.14|Notification of Acceptance |    
20.10.14|Organizational Meeting | Dr. Ralf Krestel    
3.11.14|Short Presentations| All Students    
8.12.14 |Collaborative Recommendation |    
8.12.14 |Content-Based and Knowledge-Based Recommendation |    
15.12.14 |Hybrid Recommendation |    
15.12.14 |Explanations in and Evaluation of Recommender Systems |    
12.1.15|Attacks on Recommender Systems |    
12.1.15|Recommender Systems 2.0 |    
26.1.15|Final Discussion |    
16.3.15|Report Deadline|    

Slides and Reports

You can find the slides of the presentations and the summary reports in the HPI internal area.


Overview Books:

  • Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. 2010. Recommender Systems: An Introduction (1st ed.). Cambridge University Press, New York, NY, USA.
  • Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor. 2010. Recommender Systems Handbook (1st ed.). Springer-Verlag New York, Inc., New York, NY, USA.

Collaborative Recommendation:

  • John S. Breese, David Heckerman, and Carl Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (UAI'98). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 43-52.
  • Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (WWW '01). ACM, New York, NY, USA, 285-295.
  • Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work (CSCW '94). ACM, New York, NY, USA, 175-186
  • Linden, G.; Smith, B.; York, J., "Amazon.com recommendations: item-to-item collaborative filtering," Internet Computing, IEEE , vol.7, no.1, pp.76,80, Jan/Feb 2003.

Content-Based Recommendation:

  • Michael Pazzani and Daniel Billsus. 1997. Learning and Revising User Profiles: The Identification of Interesting Web Sites. Mach. Learn. 27, 3 (June 1997), 313-331.
  • Michael Pazzani and Daniel Billsus. 2007. Content-based recommendation systems. In The adaptive web. Lecture Notes In Computer Science, Vol. 4321. Springer-Verlag, Berlin, Heidelberg 325-341.

Knowledge-Based Recommendation:

  • R. Burke, Knowledge-based Recommender Systems, Encyclopedia of Library and Information Science, 69(32):180-200, 2000.
  • A. Felfernig and R. Burke. 2008. Constraint-based recommender systems: technologies and research issues. In Proceedings of the 10th international conference on Electronic commerce (ICEC '08). ACM, New York, NY, USA, , Article 3 , 10 pages.

Hybrid Recommendation:

  • Marko Balabanović and Yoav Shoham. 1997. Fab: content-based, collaborative recommendation. Commun. ACM 40, 3 (March 1997), 66-72.
  • Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. on Knowl. and Data Eng. 17, 6 (June 2005), 734-749.
  • Robin Burke. 2007. Hybrid web recommender systems. In The adaptive web, Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl (Eds.). Lecture Notes In Computer Science, Vol. 4321. Springer-Verlag, Berlin, Heidelberg 377-408.

Explanations in Recommender Systems:

  • William F. Brewer, Clark A. Chinn, and Ala Samarapungavan. 1998. Explanation in Scientists and Children. Minds Mach. 8, 1 (February 1998), 119-136.
  • N. Tintarev, J. Masthoff. 2007. A Survey of Explanations in Recommender Systems. In Data Engineering Workshop, IEEE 23rd International Conference on Data Engineering, pp.801,810.

Evaluating Recommender Systems:

  • Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 1 (January 2004), 5-53.
  • Asela Gunawardana and Guy Shani. 2009. A Survey of Accuracy Evaluation Metrics of Recommendation Tasks. J. Mach. Learn. Res. 10 (December 2009), 2935-2962.

Attacks on Recommender Systems:

  • Shyong K. Lam and John Riedl. 2004. Shilling recommender systems for fun and profit. In Proceedings of the 13th international conference on World Wide Web (WWW '04). ACM, New York, NY, USA, 393-402.
  • Bamshad Mobasher, Robin Burke, Runa Bhaumik, and Chad Williams. 2007. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7, 4, Article 23 (October 2007).

Trust in Recommender Systems

  • John O'Donovan and Barry Smyth. 2005. Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces (IUI '05). ACM, New York, NY, USA, 167-174.

Context-aware Recommender Systems

  • Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin. 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23, 1 (January 2005), 103-145.

Recommender Systems 2.0:

  • Shilad Sen, Jesse Vig, and John Riedl. 2009. Tagommenders: connecting users to items through tags. In Proceedings of the 18th international conference on World wide web (WWW '09). ACM, New York, NY, USA, 671-680.
  • Ralf Krestel, Peter Fankhauser, and Wolfgang Nejdl. 2009. Latent dirichlet allocation for tag recommendation. In Proceedings of the third ACM conference on Recommender systems (RecSys '09). ACM, New York, NY, USA, 61-68.