Prof. Dr. Felix Naumann

Seminar Recommender Systems

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.

In the first half, we will look (in theory and practice) at different aspects of recommender systems and fundamental algorithms of the field.

In the second half, students will pick a research paper from the 2018 ACM Conference on Recommender Systems and present the findings in the seminar.

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

The seminar takes place on Mondays at 11:00 on Campus II in Room F-2.11


Students should read this short article on recommender systems before the course.


Time: Monday, 11:00 on Campus II, F 2.11

05.11.Collaborative Filtering
  1. The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games
  2. Judging Similarity: A User-Centric Study of Related Item Recommendations
  3. Why I like it: Multi-task Learning for Recommendation and Explanation
  1. Calibrated Recommendations
  2. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
  1. Enhancing Structural Diversity in Social Networks by Recommending Weak Ties
  2. Deep Reinforcement Learning for Page-wise Recommendations

(subject to change)


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 Filtering:

  • 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.


  • 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.
  • Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems (RecSys '10). ACM, New York, NY, USA, 257-260.


  • 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.


  • Marko Balabanović and Yoav Shoham. 1997. Fab: content-based, collaborative recommendation. Commun. ACM 40, 3 (March 1997), 66-72.
  • Robin Burke. 2007. Hybrid web recommender systems. In The adaptive web. Lecture Notes In Computer Science, Vol. 4321. Springer-Verlag, Berlin, Heidelberg 377-408.


  • Umberto Panniello, Alexander Tuzhilin, and Michele Gorgoglione. 2014. Comparing context-aware recommender systems in terms of accuracy and diversity. User Modeling and User-Adapted Interaction 24, 1-2 (February 2014), 35-65


  • 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, 801-810.


  • 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).

(subject to change)