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
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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
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4m 10sLearn what collaborative filtering is with regards to the users themselves. FREE ACCESS
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5m 22sNow 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: Items4m 17sNow 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 16m 26sCombine 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 26m 31sContinue to learn more about collaboritve filtering of users and items together FREE ACCESS
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6. Using Comparisons, Rankings and User-Items 15m 46sStart to bring together all previous knowledge to form your recommendation algorithm FREE ACCESS
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7. Using Comparisons, Rankings and User-Items 27m 14sFinish off learning about how all previous methods combine FREE ACCESS
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8. Graphical Models and Neural Networks 16m 8sLearn about some more complicated methods of recommendation systems FREE ACCESS
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9. Graphical Models and Neural Networks 26m 59sWrap up learning about recommendation algorithms involving graphical models FREE ACCESS
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10. Using Side-Information8m 14sLearn about how side information can help bolster the effectiveness of your recommendation algorithm FREE ACCESS
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11. 20 Questions and Active Learning7m 45sFind out how active learning plays a role in recommendation systems FREE ACCESS
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12. Building Recommendation Systems10m 57sLearn how to build an actual recommendation system FREE ACCESS
