In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Empfehlungsdienste
Nearest Neighbor Collaborative Filtering
von
Über diesen Kurs
7,730 kürzliche Aufrufe
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 5 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 12 Stunden zum Abschließen
Empfohlen: 10 hours/week
Englisch
Untertitel: Englisch
von
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.
Lehrplan - Was Sie in diesem Kurs lernen werden
13 Minuten zum Abschließen
Preface
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
13 Minuten zum Abschließen
1 Video (Gesamt 3 min), 1 Lektüre
1 Video
1 Lektüre
Course Structure Outline10m
1 Stunde zum Abschließen
User-User Collaborative Filtering Recommenders Part 1
1 Stunde zum Abschließen
5 Videos (Gesamt 85 min)
5 Videos
Configuring User-User Collaborative Filtering9m
Influence Limiting and Attack Resistance; Interview with Paul Resnick21m
Trust-Based Recommendation; Interview with Jen Golbeck15m
Impact of Bad Ratings; Interview with Dan Cosley13m
5 Stunden zum Abschließen
User-User Collaborative Filtering Recommenders Part 2
5 Stunden zum Abschließen
2 Videos (Gesamt 13 min), 2 Lektüren, 3 Quiz
2 Videos
Programming Assignment - Programming User-User Collaborative Filtering4m
2 Lektüren
Assignment Instructions: User-User CF10m
Introducing User-User CF Programming Assignment10m
2 praktische Übungen
User-User CF Answer Sheet48m
User-User Collaborative Filtering Quiz20m
1 Stunde zum Abschließen
Item-Item Collaborative Filtering Recommenders Part 1
1 Stunde zum Abschließen
6 Videos (Gesamt 70 min)
6 Videos
Item-Item Algorithm16m
Item-Item on Unary Data6m
Item-Item Hybrids and Extensions4m
Strengths and Weaknesses of Item-Item Collaborative Filtering9m
Interview with Brad Miller16m
4 Stunden zum Abschließen
Item-Item Collaborative Filtering Recommenders Part 2
4 Stunden zum Abschließen
2 Videos (Gesamt 10 min), 2 Lektüren, 5 Quiz
2 Videos
Programming Assignment - Programming Item-Item Collaborative Filtering4m
2 Lektüren
Item-Based CF Assignment Instructions10m
Introducing Item-Item CF Programming Assignment10m
4 praktische Übungen
Item Based Assignment Part l10m
Item Based Assignment Part II10m
Item Based Assignment Part III10m
Item Based Assignment Part IV10m
2 Stunden zum Abschließen
Advanced Collaborative Filtering Topics
2 Stunden zum Abschließen
5 Videos (Gesamt 73 min)
5 Videos
Recommending for Groups: Interview with Anthony Jameson14m
Threat Models11m
Explanations16m
Explanations, Part II: Interview with Nava Tintarev17m
1 praktische Übung
Item-Based and Advanced Collaborative Filtering Topics Quiz20m
Bewertungen
Top-Bewertungen von NEAREST NEIGHBOR COLLABORATIVE FILTERING
von NSDec 11, 2019
i found this course very helpful and informative. it explains the theory while providing real-world examples on recommender systems. the assignment helps in clearing up any confusion with the material
von SSMar 30, 2019
Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.
von DGFeb 1, 2020
I found this course very informative and clears lot of concept in Item based and used based collaborative filtering. Spreadsheet assignment helped me to clearly understand the algorithms.
von NRFeb 3, 2018
Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!
von ARAug 3, 2017
Awesome as always, Joe and Michael rock. The interview with Brad Miller was stellar, felt like listening to the legends of rock-n-roll!
von DRJun 14, 2017
Very satisfied to do this, the videos are too long, very good quality and a lot of practical information.\n\nI love it!
von CJJul 16, 2017
Very good course, there is a glaring error in Week 4s assignment. But if you check the forums it can be easily solved
von PSJan 7, 2017
I love it. Would be cool to be able download all materials in one big .zip file (e.g for searching using grep) ;-)
Über den Spezialisierung Empfehlungsdienste
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics.
The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit.
By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Sobald Sie sich für ein Zertifikat angemeldet haben, haben Sie Zugriff auf alle Videos, Quizspiele und Programmieraufgaben (falls zutreffend). Aufgaben, die von anderen Kursteilnehmern bewertet werden, können erst dann eingereicht und überprüft werden, wenn Ihr Unterricht begonnen hat. Wenn Sie sich den Kurs anschauen möchten, ohne ihn zu kaufen, können Sie womöglich auf bestimmte Aufgaben nicht zugreifen.
Was bekomme ich, wenn ich diese Spezialisierung abonniere?
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.
Wie erfolgen Rückerstattungen?
Ist finanzielle Unterstützung möglich?
Haben Sie weitere Fragen? Besuchen Sie das Hilfe-Center für Teiln..