Recommender Systems: Methods and Applications


You can review your exams on 22.03.18 at 15:30 in the room G29-128.



DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Mon. 13:00 bis 15:00 weekly G29-307 (Verwaltung durch FIN, Pandemiebestuhlung: 42 Plätze) Spiliopoulou   60
Übung (Ü) - Exercise - Dates/Times/Location:
Tue.13:00 bis 15:00weeklyG22A-208 (40 Pl.)Matuszyk 40

Overview (from LSF)

Learning Content

In this course we elaborate on the role of recommenders as a primary means of improving a user's or customer's experience, while increasing company revenue. The course covers learning methods for the recommender core, approaches for the design and evaluation of recommenders, and specific application areas of recommenders.

This course is a followup of the bachelor course CRM/RecSys, which contains an introduction to Customer Relationship Management and to Recommendation Engines, including two core methods for model learning (collaborative filtering and content-based modeling) and basics on evaluating recommenders. In this course, we consider advanced learning methods and advanced instruments for guaranteeing the goodness of the recommender core.

Descriptionin English


    F. Ricci, L. Rokach, B. Shapira (eds). Recommender Systems Handbook. Springer 2011, esp:

Learning methods and applications

  • Ch4: A Comprehensive Survey of Neighborhood-based Recommendation Methods
  • Ch5: Advances in Collaborative Filtering
  • Ch19: Social Tagging Recommender Systems
  • Ch22: Aggregation of Preferences in Recommender Systems

Recommender design and evaluation

  • Ch11: Matching Recommendation Technologies and Domains
  • Ch14: Creating More Credible and Persuasive Recommender Systems:
 The Influence of Source Characteristics on Recommender
 System Evaluations
  • Ch15: Designing and Evaluating Explanations for

2) Scientific articles, mainly from the ACM conferences:

  • International Conf. on Recommender Systems (RecSys)
  • International Conf. on Information & Knowledge Management (CIKM)

Background in data mining is of advantage. This course is also appropriate for students who have heard the CRM/RecSys bachelor course.

Description Recommender Systems: Methods and Applications





Letzte Änderung: 27.02.2018 - Ansprechpartner:

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