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.
Ce cours fait partie de la Spécialisation Systèmes de recommandation
Offert par
Spécialisation Systèmes de recommandationUniversité du MinnesotaÀ propos de ce cours
6,749 consultations récentes
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 5 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Approx. 15 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 5 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Approx. 15 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Offert par
Université du 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.
Programme du cours : ce que vous apprendrez dans ce cours
13 minutes pour terminer
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 minutes pour terminer
1 vidéo (Total 3 min), 1 lecture
1 vidéo
Course Introduction3 min
1 lecture
Course Structure Outline10 min
1 heure pour terminer
User-User Collaborative Filtering Recommenders Part 1
1 heure pour terminer
5 vidéos (Total 85 min)
5 vidéos
Configuring User-User Collaborative Filtering9 min
Influence Limiting and Attack Resistance; Interview with Paul Resnick21 min
Trust-Based Recommendation; Interview with Jen Golbeck15 min
Impact of Bad Ratings; Interview with Dan Cosley13 min
5 heures pour terminer
User-User Collaborative Filtering Recommenders Part 2
5 heures pour terminer
2 vidéos (Total 13 min), 2 lectures, 3 quiz
2 vidéos
Programming Assignment - Programming User-User Collaborative Filtering4 min
2 lectures
Assignment Instructions: User-User CF10 min
Introducing User-User CF Programming Assignment10 min
2 exercices pour s'entraîner
User-User CF Answer Sheet30 min
User-User Collaborative Filtering Quiz30 min
1 heure pour terminer
Item-Item Collaborative Filtering Recommenders Part 1
1 heure pour terminer
6 vidéos (Total 70 min)
6 vidéos
Item-Item Algorithm16 min
Item-Item on Unary Data6 min
Item-Item Hybrids and Extensions4 min
Strengths and Weaknesses of Item-Item Collaborative Filtering9 min
Interview with Brad Miller16 min
6 heures pour terminer
Item-Item Collaborative Filtering Recommenders Part 2
6 heures pour terminer
2 vidéos (Total 10 min), 2 lectures, 5 quiz
2 vidéos
Programming Assignment - Programming Item-Item Collaborative Filtering4 min
2 lectures
Item-Based CF Assignment Instructions10 min
Introducing Item-Item CF Programming Assignment10 min
4 exercices pour s'entraîner
Item Based Assignment Part l30 min
Item Based Assignment Part II30 min
Item Based Assignment Part III30 min
Item Based Assignment Part IV30 min
2 heures pour terminer
Advanced Collaborative Filtering Topics
2 heures pour terminer
5 vidéos (Total 73 min)
5 vidéos
The Cold Start Problem13 min
Recommending for Groups: Interview with Anthony Jameson14 min
Threat Models11 min
Explanations16 min
Explanations, Part II: Interview with Nava Tintarev17 min
1 exercice pour s'entraîner
Item-Based and Advanced Collaborative Filtering Topics Quiz30 min
Avis
Meilleurs avis pour NEAREST NEIGHBOR COLLABORATIVE FILTERING
par NS11 déc. 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
par SS30 mars 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.
par DG1 févr. 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.
par NR3 févr. 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!
À propos du Spécialisation Systèmes de recommandation
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.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Is financial aid available?
Puis-je obtenir des crédits universitaires si je réussis le Cours ?
D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.
