Understand matrix completion using dot products.
- [Instructor] Let's write the main code…for our recommendation system.…Open up factor_review_matrix.py.…First, I'll load up the review dataset into a data frame…called raw_dataset_df…by using pandas read_csv function.…Then we use the pandas pivot table function…to build the review matrix.…At this point,…ratings_df contains a sparse array of reviews.…Next, we want to factor the array…to find the user attributes matrix…and the movie attributes matrix…that we can multiply back together…to recreate the ratings data.…
To do this,…we'll use the low rank matrix factorization algorithm.…I've included an implementation of this…in matrix_factorization_utilities.py.…We'll talk more about how it works in the next video,…but let's go ahead and use it.…First, we pass in the ratings data,…but we'll call pandas as matrix function…to make sure we pass then as a numpy matrix data type.…Next, this method takes in a parameter called num_features.…Num_features controls how many latent features…to generate for each user and each 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: Code the recommendation system