Understand the idea of recommendations based on user attributes.
- [Instructor] If we can fill in all the blanks…in our user review matrix,…we'll know how each user would rate each movie.…Then, we can use that information to recommend movies…that are highly rated.…Let's learn how to complete this matrix by hand,…assuming we have extra information about each user…and each movie.…This will teach us the basic idea…that we'll use to calculate each user's interest…in each movie.…To understand how to predict a user's rating,…let's think about how someone decides…what rating to give a movie.…Every human is unique.…There's probably no way to completely understand…the thought process that went into a certain rating.…
So let's assume that a user's rating…is a reflection of how much a particular movie appeals…to that user's unique set of interests.…This gives us a way to calculate a user's rating.…First, we'll create a model of how much a movie would appeal…to every possible interest.…Then, we'll make a model of a user's specific interests.…Finally, we can calculate the user's rating…based on how well the user's interest matched the movie.…
Author
Adam Geitgey
Released
4/10/2017Recommendation systems are a key part of almost every modern consumer website. The systems help drive customer interaction and sales by helping customers discover products and services they might not ever find themselves. The course uses the free, open source tools Python 3.5, pandas, and numpy. By the end of the course, you'll be equipped to use machine learning yourself to solve recommendation problems. What you learn can then be directly applied to your own projects.
- Building a machine learning system
- Training a machine learning system
- Refining the accuracy of the machine learning system
- Evaluating the recommendations received
Skill Level Intermediate
Duration
Views
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Introduction
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Welcome1m 1s
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Set up environment2m 15s
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1. The Basics of Making Recommendations
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2. Ways of Making Recommendations
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3. Getting to Know Our Tools
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4. Building the Framework for Our Recommendation System
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5. Collaborative Filtering with Matrix Factorization
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6. Testing Our System
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Use regularization1m 52s
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7. Using the Recommendation System in a Real World Program
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Find similar products1m 59s
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Conclusion
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Wrap up47s
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Video: A simple way to predict missing user ratings