Über diesen Kurs
4.5
399 Bewertungen
76 Bewertungen

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Mittel“

Ca. 16 Stunden zum Abschließen

Empfohlen: 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. ...

Englisch

Untertitel: Englisch

Kompetenzen, die Sie erwerben

Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Mittel“

Ca. 16 Stunden zum Abschließen

Empfohlen: 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. ...

Englisch

Untertitel: Englisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
1 Stunde zum Abschließen

Preface

This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization....
2 Videos (Gesamt 41 min), 1 Lektüre
2 Videos
Intro to Course and Specialization13m
1 Lektüre
Notes on Course Design and Relationship to Prior Courses10m
3 Stunden zum Abschließen

Introducing Recommender Systems

This module introduces recommender systems in more depth. It includes a detailed taxonomy of the types of recommender systems, and also includes tours of two systems heavily dependent on recommender technology: MovieLens and Amazon.com. There is an introductory assessment in the final lesson to ensure that you understand the core concepts behind recommendations before we start learning how to compute them....
9 Videos (Gesamt 147 min), 2 Lektüren, 2 Quiz
9 Videos
Preferences and Ratings17m
Predictions and Recommendations16m
Taxonomy of Recommenders I27m
Taxonomy of Recommenders II21m
Tour of Amazon.com21m
Recommender Systems: Past, Present and Future16m
Introducing the Honors Track7m
Honors: Setting up the development environment10m
2 Lektüren
About the Honors Track10m
Downloads and Resources10m
2 praktische Übungen
Closing Quiz: Introducing Recommender Systems20m
Honors Track Pre-Quiz2m
Woche
2
7 Stunden zum Abschließen

Non-Personalized and Stereotype-Based Recommenders

In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension. ...
7 Videos (Gesamt 111 min), 5 Lektüren, 9 Quiz
7 Videos
Summary Statistics I16m
Summary Statistics II22m
Demographics and Related Approaches13m
Product Association Recommenders19m
Assignment #1 Intro Video14m
Assignment Intro: Programming Non-Personalized Recommenders17m
5 Lektüren
External Readings on Ranking and Scoring10m
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10m
Assignment Intro: Programming Non-Personalized Recommenders10m
LensKit Resources10m
Rating Data Information10m
8 praktische Übungen
Assignment #1: Response #1: Top Movies by Mean Rating10m
Assignment #1: Response #2: Top Movies by Count10m
Assignment #1: Response #3: Top Movies by Percent Liking10m
Assignment #1: Response #4: Association with Toy Story10m
Assignment #1: Response #5: Correlation with Toy Story10m
Assignment #1: Response #6: Male-Female Differences in Average Rating10m
Assignment #1: Response #7: Male-Female differences in Liking8m
Non-Personalized Recommenders20m
Woche
3
3 Stunden zum Abschließen

Content-Based Filtering -- Part I

The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems. ...
8 Videos (Gesamt 156 min)
8 Videos
TFIDF and Content Filtering24m
Content-Based Filtering: Deeper Dive26m
Entree Style Recommenders -- Robin Burke Interview13m
Case-Based Reasoning -- Interview with Barry Smyth13m
Dialog-Based Recommenders -- Interview with Pearl Pu21m
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11m
Beyond TFIDF -- Interview with Pasquale Lops21m
Woche
4
6 Stunden zum Abschließen

Content-Based Filtering -- Part II

The assessments for content-based filtering include an assignment where you compute three types of profile and prediction using a spreadsheet and a quiz on the topics covered. The assignment is in three parts -- a written assignment, a video intro, and a "quiz" where you provide answers from your work to be automatically graded....
2 Videos (Gesamt 26 min), 3 Lektüren, 3 Quiz
2 Videos
Honors: Intro to programming assignment10m
3 Lektüren
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)20m
Tools for Content-Based Filtering10m
CBF Programming Intro10m
2 praktische Übungen
Assignment #2 Answer Form20m
Content-Based Filtering20m
1 Stunde zum Abschließen

Course Wrap-up

We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization). ...
2 Videos (Gesamt 45 min), 1 Lektüre
2 Videos
Psychology of Preference & Rating -- Interview with Martijn Willemsen31m
1 Lektüre
Related Readings10m
4.5
76 BewertungenChevron Right

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nahm einen neuen Beruf nach Abschluss dieser Kurse auf

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ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs

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Top-Bewertungen

von BSFeb 13th 2019

One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.

von DPDec 8th 2017

Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).

Dozenten

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Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
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Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University

Über University of Minnesota

The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations....

Über die Spezialisierung Empfehlungsdienste

This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project....
Empfehlungsdienste

Häufig gestellte Fragen

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  • Wenn Sie sich für den Kurs anmelden, erhalten Sie Zugriff auf alle Kurse der Spezialisierung und Sie erhalten nach Abschluss aller Arbeiten ein Zertifikat. Ihr elektronisches Zertifikat wird zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.

  • This specialization is a substantial extension and update of our original introductory course. It involves about 60% new and extended lectures and mostly new assignments and assessments. This course specifically has added material on stereotyped and demographic recommenders and on advanced techniques in content-based recommendation.

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