date_range Débute le 3 septembre 2013
event_note Se termine le 10 décembre 2013
list 14 séquences
assignment Niveau : Introductif
chat_bubble_outline Langue : Anglais
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Les infos clés

credit_card Formation gratuite
verified_user Certification gratuite
timer 56 heures de cours

En résumé

This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply the same techniques in your own systems!

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Le programme

Topics covered:
Week 1:
Introduction to Course and to Recommender Systems
Weeks 2 and 3:
Non-Personalized Recommenders
Understanding Ratings, Predictions, and Recommendations
Scales and Normalization
Interview with Anthony Jameson (DFKI AI Labs)
Weeks 4 and 5:
Content-Based Recommenders
Inferring Preferences
Unary Ratings
Knowledge-Based Recommenders
Introduction to LensKit Toolkit
Interviews with Robin Burke (DePaul University) and Barry Smyth (University College Dublin)
Weeks 6 and 7:
Collaborative Filtering
User-User k-Nearest Neighbor Approach
Tuning CF Algorithms
Interviews with Paul Resnick (University of Michigan), Jen Golbeck (University of Maryland) and Dan Cosley(Cornell University)
Weeks 8 and 9:
Evaluation and Metrics;
Error Metrics;
Decision-Support Metrics
Comparative Evaluation: Dead Data vs. Laboratory vs. Field Study
User-Centered Metrics and Evaluation
Data Sets
Interview with Neal Lathia (University of Cambridge)
Weeks 10 and 11:
Collaborative Filtering II
Item-Item k-Nearest Neighbor
Business Rules
Adjustments for Serendipity and Diversity
Performance Comparisons
Hybrid Algorithms
Interviews with Brad Miller (Luther College) and Robin Burke (DePaul University)
Weeks 12 and 13:
Dimensionality Reduction Recommenders
Concepts behind Latent Semantic Analysis and Singular Value Decomposition
Week 14:
Alternative Recommender Approaches
Interactive Recommenders
Critique and Dialog-based Approaches
Advanced Topics
Resources
Interview with Anthony Jameson (DFKI AI Labs), Francesco Ricci (Free University of Bozen-Bolzano), Xavier Amatriain (NetFlix) and Anmol Bhasin (LinkedIn)
Conclusion

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Les intervenants

  • - Dept. of Computer Science, Texas State University
  • Joseph Konstan - Computer Science and Engineering
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Le concepteur

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.
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La plateforme

Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

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