Collaborative Filtering & Personalized Recommendations

Everyone
  • 12 videos | 1h 19m 49s
  • Includes Assessment
Learn the limitations of traditional prediction and the fundamentals of personalized recommendations. Also learn the many variations such as the use of side information, dynamic models or active models to develop even more accurate recommendation systems

WHAT YOU WILL LEARN

  • Understand the basics behind personalization using collaborative filtering using similar users
    Understand the nearest neighbors technique.
    Understand the basics behind personalization using collaborative filtering using similar items
    Know how to use collaborative filtering with both users and items
    Learn more about collaborative filtering with both users and items
    Learn how all previous methods combine
  • Know how all previous methods work together
    Understand recommendation systems using graphical models and neural networks
    Further understand the graphical models associated with recommendation systems
    Know how to use side information to refine a recommendation system
    Understand how active learning plays a role in recommendation systems
    Understand how to build an actual recommendation system

IN THIS COURSE

  • 4m 10s
    Learn what collaborative filtering is with regards to the users themselves. FREE ACCESS
  • 5m 22s
    Now that you know what collaborative filtering is, learn how nearest neighbors helps with that. FREE ACCESS
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    3.  Personalizing Using Collaborative Filtering: Items
    4m 17s
    Now that you have seen collaborative filtering using similar users, learn about the technique with regards to the items themselves FREE ACCESS
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    4.  Collaborative Filtering By Similar Users & Items 1
    6m 26s
    Combine both what you know about collaborative filtering of users and items together FREE ACCESS
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    5.  Collaborative Filtering By Similar Users & Items 2
    6m 31s
    Continue to learn more about collaboritve filtering of users and items together FREE ACCESS
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    6.  Using Comparisons, Rankings and User-Items 1
    5m 46s
    Start to bring together all previous knowledge to form your recommendation algorithm FREE ACCESS
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    7.  Using Comparisons, Rankings and User-Items 2
    7m 14s
    Finish off learning about how all previous methods combine FREE ACCESS
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    8.  Graphical Models and Neural Networks 1
    6m 8s
    Learn about some more complicated methods of recommendation systems FREE ACCESS
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    9.  Graphical Models and Neural Networks 2
    6m 59s
    Wrap up learning about recommendation algorithms involving graphical models FREE ACCESS
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    10.  Using Side-Information
    8m 14s
    Learn about how side information can help bolster the effectiveness of your recommendation algorithm FREE ACCESS
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    11.  20 Questions and Active Learning
    7m 45s
    Find out how active learning plays a role in recommendation systems FREE ACCESS
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    12.  Building Recommendation Systems
    10m 57s
    Learn how to build an actual recommendation system FREE ACCESS

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