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
Nearest Neighbor Collaborative Filtering
proposé par
À propos de ce cours
7,455 consultations récentes
Les étudiants prenant part à ce Course sont
- Data Scientists
- Machine Learning Engineers
- Data Engineers
- Research Assistants
- Business Analysts
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
113 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.
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
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
Semaine
25 heures pour terminer
User-User Collaborative Filtering Recommenders Part 2
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 Sheet48 min
User-User Collaborative Filtering Quiz20 min
Semaine
31 heure pour terminer
Item-Item Collaborative Filtering Recommenders Part 1
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
Semaine
44 heures pour terminer
Item-Item Collaborative Filtering Recommenders Part 2
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 l10 min
Item Based Assignment Part II10 min
Item Based Assignment Part III10 min
Item Based Assignment Part IV10 min
2 heures pour terminer
Advanced Collaborative Filtering Topics
5 vidéos (Total 73 min), 1 quiz
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 Quiz20 min
4.3
51 avisPrincipaux examens pour Nearest Neighbor Collaborative Filtering
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.
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!
Enseignants
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
Michael D. Ekstrand
Assistant ProfessorDept. of Computer Science, Boise State University
À propos de 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.
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.
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.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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