Recommender Systems (Wintersemester 2018/2019)
Dozent:
Prof. Dr. Ralf Krestel
(Information Systems)
Tutoren:
Julian Risch
Tim Repke
Website zum Kurs:
https://hpi.de/naumann/teaching/teaching/ws-1819/recommender-systems.html
Allgemeine Information
- Semesterwochenstunden: 2
- ECTS: 3
- Benotet:
Ja
- Einschreibefrist: 26.10.2018
- Lehrform: Seminar
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Deutsch
- Maximale Teilnehmerzahl: 6
Studiengänge, Modulgruppen & Module
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-K Konzepte und Methoden
- DATA: Data Analytics
- HPI-DATA-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-S Spezialisierung
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
Beschreibung
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.
Voraussetzungen
Students should read this short article on recommender systems.
Literatur
- 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.
Leistungserfassung
Summarize a selected topic and present a recent research paper.
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