Recommender Systems: Methods and Applications

 

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

 

Timetable

DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Mon. 13:00 bis 15:00 weekly G29-307 Spiliopoulou Lehrpreisträger/-in   60
Übung (Ü) - Exercise - Dates/Times/Location:
Tue.13:00 bis 15:00weeklyG22A-208 (40 Plätze)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
Literature

Literature:

    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
RecommenderSystems


2) Scientific articles, mainly from the ACM conferences:

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

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

 

Lecture:

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Last Modification: 27.02.2018 - Contact Person:

Sie können eine Nachricht versenden an: Dr.-Ing. Pawel Matuszyk
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