See the results of your work.
- [Instructor] Now that we have…a working product recommendation system…let's see what results we get for different users.…Let's take a look at make_recommendations.py.…First we'll read in the data set…using panda's read csv command.…We'll also load the movie list using read csv…so we have access to the movie titles as well.…Then we'll use panda's pivot table function…to create the review matrix,…then we'll factor the review matrix…to get the U and M matrices.…Then we'll multiply U and M…to create the predicted ratings for each user.…Now that we have predicted ratings we can make predictions.…
Here we'll prompt for a user ID…that way the user can type in any user ID…to see recommendations for different users.…Before we print out the users recommended movies,…let's print out the movies the user…has already rated themself.…We can look them up in the raw data set df data frame.…We'll ask pandas to filter down the list…to the entries where the user ID…is the same one as the user just typed in.…Next let's join this list of reviews with 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: Explore our system’s recommendations