31-03-2021: We are planning an 'Onlineveranstaltung – Mix aus synchron und asynchron' lecture due to the COVID-19 situation. The lecture and exercise will be video-recorded and made available for download. An addition synchronous sessions will be held for both, lecture and exercise via Zoom.
Further information are available on Moodle.
A registration in the LSF to the groups is obligatory (hygiene concept) and simplifies the communication outside of Moodle.
Eine Registrierung im LSF zu den Gruppen ist verpflichtend (Hygiene-Konzept) und vereinfacht die Kommunikation außerhab von Moodle.
All course materials, announcements, information and links for all students will be provided on Moodle
|Vorlesung(V) - Lecture - Dates/Times/Location:|
|Tue.||15:00 bis 17:00||weekly||Spiliopoulou|
|Übung (Ü) - Exercise - Dates/Times/Location:|
|Mon.||13:00 bis 15:00||weekly||Tutor|
Diese Übung wird in deutsch stattfinden. Weitere Informationen zu Tag und Uhrzeit folgen.
|Tue.||11:00 bis 13:00||weekly||Jamaludeen|
Overview (from LSF)
Outline of the course
Part I - Basics of recommenders:
Part II: Neighbourhood-based learning methods
Part III: Exploiting text content
Part IV: The surroundings of a recommender
Part V: Study of scientific papers – compulsory for the 6 ECTS version, optional for the 5 ECTS version
All students: Having attended data mining or machine learning or an AI course is of advantage, although you may catch up with home reading. You must refresh your secondary school background in statistics.
Goal of this course is to make you familiar with recommenders. You will learn what requirements are placed to a recommender by the business operating it and by the users interacting with it, and you will become proficient in methods used to meet these requirements.
A recommender has a front-end service responsible for the interaction, and a back-end machine learning core that derives the recommendations to be presented to the users and learns from the users' behavior. Most part of the course is on the back-end: you will become familiar with basic and advanced methods that model the recommendation task as an optimization problem and deliver solutions for it.
A recommender learns from information on the items to be recommended and the users to be served. You will see methods that extract such information from data – mainly opinionated texts.
Book of the course:
Core papers on the underpinnings of specific course topics:
Further papers are cited in the materials of the course.
The additional materials for Part V will be announced during the course.
The course RECSYS can be examined for 5 ECTS or for 6 ECTS.
To acquire the 5 ECTS, enroll for the 5-ECTS-exam.
For the 6th credit point, you make an additional assignment which involves homework. There are two options for this assignment:
For the 6th credit point, Option DEFAULT is assumed for all students who did not actively select Option CHOICE, including those that started with the CHOICE assignment but did not finish it.
The RECSYS exam is oral by default. Depending on the number of students attending the course, it may be turned to a written exam (as usual for many FIN courses).
|Target Group||<p>RECSYS is for bachelor students in high semesters and for master students, as follows:</p><ul><li>5 ECTS: Bachelor INF, INGINF, CV and Bachelor WIF</li><li>6 ECTS (additional materials): Master INF, INGINF, WIF, DigiEng, Data Science Master DKE</li></ul><p>The exercise class for the bachelor degrees is on German, for the master degrees on English.</p><p>Under Exam you find additional information on how you acquire the 5 ECTS, resp. 6 ECTS <a name="Uebungen"></a></p><p> </p>|