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.
Este curso forma parte de Programa Especializado - Sistemas de recomendación
Nearest Neighbor Collaborative Filtering
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Aprox. 12 horas para completar
Sugerido: 10 hours/week
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Universidad de 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.
Programa - Qué aprenderás en este curso
13 minutos para completar
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 minutos para completar
1 video (Total 3 minutos), 1 lectura
1 video
1 lectura
Course Structure Outline10m
1 hora para completar
User-User Collaborative Filtering Recommenders Part 1
1 hora para completar
5 videos (Total 85 minutos)
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 horas para completar
User-User Collaborative Filtering Recommenders Part 2
5 horas para completar
2 videos (Total 13 minutos), 2 lecturas, 3 cuestionarios
2 videos
Programming Assignment - Programming User-User Collaborative Filtering4m
2 lecturas
Assignment Instructions: User-User CF10m
Introducing User-User CF Programming Assignment10m
2 ejercicios de práctica
User-User CF Answer Sheet48m
User-User Collaborative Filtering Quiz20m
1 hora para completar
Item-Item Collaborative Filtering Recommenders Part 1
1 hora para completar
6 videos (Total 70 minutos)
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 horas para completar
Item-Item Collaborative Filtering Recommenders Part 2
4 horas para completar
2 videos (Total 10 minutos), 2 lecturas, 5 cuestionarios
2 videos
Programming Assignment - Programming Item-Item Collaborative Filtering4m
2 lecturas
Item-Based CF Assignment Instructions10m
Introducing Item-Item CF Programming Assignment10m
4 ejercicios de práctica
Item Based Assignment Part l10m
Item Based Assignment Part II10m
Item Based Assignment Part III10m
Item Based Assignment Part IV10m
2 horas para completar
Advanced Collaborative Filtering Topics
2 horas para completar
5 videos (Total 73 minutos)
5 videos
Recommending for Groups: Interview with Anthony Jameson14m
Threat Models11m
Explanations16m
Explanations, Part II: Interview with Nava Tintarev17m
1 ejercicio de práctica
Item-Based and Advanced Collaborative Filtering Topics Quiz20m
Revisiones
Principales revisiones sobre NEAREST NEIGHBOR COLLABORATIVE FILTERING
por 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
por 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.
por 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.
por 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!
por 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!
por 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!
por 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
por 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) ;-)
por SKJan 16, 2018
Provides a good overview of item based and user based collaborative filtering approaches.
por SPFeb 13, 2017
Awesome Professors!Great Material.Very thankful to Coursera for providing this course.
por APDec 2, 2019
Overall good, except for assignment 2 which was poorly explained on one of the parts
por ADAug 2, 2017
Loved it...many thanks Prof. Joe for bringing this content to Coursera
Acerca de Programa especializado Sistemas de recomendación
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.
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Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
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Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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