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Recommender Systems

Recommender Systems
type: Vorlesung (V) links:
semester: SS 2020
time: 2020-04-21
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau


2020-04-28
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-05-05
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-05-12
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-05-19
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-05-26
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-06-02
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-06-09
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-06-16
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-06-23
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-06-30
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-07-07
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-07-14
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau

2020-07-21
09:45 - 11:15 wöchentlich
10.11 Raum 213 ( Ersatzraum für Raum 103.2 Geb. 20.14 )
10.11 Verwaltungsgebäude, Hauptbau


lecturer: Prof. Dr. Andreas Geyer-Schulz
sws: 2
lv-no.: <a target="lvn" href="https://campus.studium.kit.edu/events/w1cZq-lITCm8bQFmO5A32Q">2540506</a>
Notes

At first, an overview of general aspects and concepts of recommender systems and its relevance for service providers and customers is given. Next, different categories of recommender systems are discussed. This includes explicit recommendations like customer reviews as well as implicit services based on behavioral data. Furthermore, the course gives a detailed view of the current research on recommender systems at the Chair of Information Services and Electronic Markets.

Learning objectives:

The student

  • is proficient in different statistical, data-mining, and game theory methods of computing implicit and explicit recommendations
  • evaluates recommender systems and compares these with related services

Workload:

The total workload for this course is approximately 135 hours (4.5 credits):

Time of attendance

  • Attending the lecture: 15 x 90min = 22h 30m
  • Attending the exercise classes: 7 x 90min = 10h 30m
  • Examination: 1h 00m

Self-study

  • Preparation and wrap-up of the lecture: 15 x 180min = 45h 00m
  • Preparing the exercises: 25h 00m
  • Preparation of the examination: 31h 00m

Sum: 135h 00m

Exam:

Assessment consists of a written exam of 1 hour length following §4 (2), 1 of the examination regulation and by submitting written papers as part of the exercise following §4 (2), 3 of the examination regulation.

The course is considered successfully taken, if at least 50 out of 100 points are acquired in the written exam. In this case, all additional points (up to 10) from excersise work will be added.

Grade: Minimum points

  • 1,0: 95
  • 1,3: 90
  • 1,7: 85
  • 2,0: 80
  • 2,3: 75
  • 2,7: 70
  • 3,0: 65
  • 3,3: 60
  • 3,7: 55
  • 4,0: 50
  • 5,0: 0
Bibliography

Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining association rules between sets of items in large databases. In Sushil Jajodia Peter Buneman, editor, Proceedings of the ACM SIGMOD International Conference on Management of Data, volume 22, Washington, D.C., USA, Jun 1993. ACM, ACM Press.

Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th Very Large Databases Conference, Santiago, Chile, pages 487 – 499, Sep 1994.

Asim Ansari, Skander Essegaier, and Rajeev Kohli. Internet recommendation systems. Journal of Marketing Research, 37:363 – 375, Aug 2000.

Christopher Avery, Paul Resnick, and Richard Zweckhauser. The market for evaluations. American Economic Review, 89(3):564 – 584, 1999.

Ibrahim Cingil, Asuman Dogac, and Ayca Azgin. A Broader Approach to Personalization. Communications of the ACM, 43(8):136 – 141, Aug 2000.

Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification. Wiley-Interscience, New York, 2 edition, 2001.

Andreas Geyer-Schulz, Michael Hahsler, and Maximilian Jahn. A customer purchase incidence model applied to recommender services. In R. Kohavi et al., editor, Proceedings of the WebKDD 2001 – Mining log data across all customer touchpoints, volume 2356 of Lecture Notes in Artificial Intelligence LNAI, pages 25–47, Berlin, 2002. ACM, Springer-Verlag.

Jon M. Kleinberg. Authoritative sources in a hyperlinked environment. JACM, 46(5):604–632, sep 1999.

Joseph Konstan, Bradley Miller, David Maltz, Jonathan Herlocker, Lee Gordon, and John Riedl. Grouplens: Applying Collaborative Filtering to Usernet News. Communications of the ACM, 40(3):77 – 87, Mar 1997.

Paul Resnick, Neophytos Iacovou, Peter Bergstrom, and John Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the conference on Computer supported cooperative work, pages 175 – 186. ACM Press, 1994.

Weiterführende Literatur:

Antoinette Alexander. The return of hardware: A necessary evil? Accounting Technology, 15(8):46 – 49, Sep 1999.

Christopher Avery and Richard Zeckhauser. Recommender systems for evaluating computer messages. Communications of the ACM, 40(3):88 – 89, Mar 1997.

Steven Bellman, Gerald Lohse, and Eric Johnson. Predictors of Online Buying Behavior. Communications of the ACM, 42(12):32 – 38, Dec 1999.

Thomas J. Blischok. Every transaction tells a story. Chain Store Age Executive with Shopping Center Age, 71(3):50–56, Mar 1995.

Hans Hermann Bock. Automatische Klassifikation. Vandenhoeck und Ruprecht, Göttingen, 1974.

Andrew S.C. Ehrenberg. Repeat-Buying: Facts, Theory and Applications. Charles Griffin & Company Ltd, London, 2 edition, 1988.

Wolfgang Gaul, Andreas Geyer-Schulz, Michael Hahsler, and Lars Schmidt-Thieme. eMarketing mittels Recommendersystemen. Marketing ZFP, 24:47 – 55, 2002.

Andreas Geyer-Schulz, Michael Hahsler, and Maximilian Jahn. myvu: a next generation recommender system based on observed consumer behavior and interactive evolutionary algorithms. In W. Gaul, O. Opitz, and M. Schader, editors, Data Analysis – Scientific Modeling and Practical Applications, volume 18 of Studies in Classification, Data Analysis and Knowledge Organization, pages 447 – 457, Heidelberg, Germany, 2000. Springer.

Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Educational and scientific recommender systems: Designing the information channels of the virtual university. International Journal of Engineering Education, 17(2):153 – 163, 2001.

Mark-Edward Grey. Recommendersysteme auf Basis linearer Regression, 2004.

John A. Hartigan. Clustering Algorithms. John Wiley and Sons, New York, 1975.

Kevin Kelly. New Rules for the New Economy: 10 Radical Strategies for a Connected World. Viking, 1998.

Taek-Hun Kim, Young-Suk Ryu, Seok-In Park, and Sung-Bong Yang. An improved recommendation algorithm in collaborative filtering. In K. Bauknecht, A. Min Tjoa, and G. Quirchmayr, editors, E-Commerce and Web Technologies, Third International Conference, Aix-en-Provence, France, volume 2455 of Lecture Notes in Computer Science, pages 254–261, Berlin, Sep 2002. Springer-Verlag.

Ron Kohavi, Brij Masand, Myra Spiliopoulou, and Jaideep Srivastava. Web mining. Data Mining and Knowledge Discovery, 6:5 – 8, 2002.

G. S. Maddala. Introduction to Econometrics. John Wiley, Chichester, 3 edition, 2001.

Andreas Mild and Martin Natter. Collaborative filtering or regression models for Internet recommendation systems? Journal of Targeting, Measurement and Analysis for Marketing, 10(4):304 – 313, Jan 2002.

Andreas Mild and Thomas Reutterer. An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. Journal of Retailing & Consumer Services, 10(3):123–133, may 2003.

Paul Resnick and Hal R. Varian. Recommender Systems. Communications of the ACM, 40(3):56 – 58, Mar 1997.

Badrul M. Sarwar, Joseph A. Konstan, Al Borchers, Jon Herlocker, Brad Miller, and John Riedl. Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In Proceedings of ACM Conference on Computer-Supported Cooperative Work, Social Filtering, Social Influences, pages 345 – 354, New York, 1998. ACM Press.

J. Ben Schafer, Joseph Konstan, and Jon Riedl. Recommender Systems in E-commerce. In Proceedings of the 1st ACM conference on Electronic commerce, pages 158 – 166, Denver, Colorado, USA, Nov 1999. ACM.

Upendra Shardanand and Patti Maes. Social information filtering: Algorithms for automating "word of mouth". In Proceedings of ACM SIGCHI, volume 1 of Papers: Using the Information of Others, pages 210 – 217. ACM, 1995.