Learn how all user's product reviews are stored in one large, two-dimentional table.
- [Instructor] Our movie review dataset…contains one row for each rating.…This is the format that reviewed data…is typically collected in,…but in order to build a recommendations system…from this data, we want to create a matrix…or two-dimensional array that shows…which movies have been rated by which users.…The matrix will have one row for each user…and one column for each movie.…Let's take a look at the code in create_review_matrix.py.…First, we're going to use Pandas read_csv function…to load the movie_ratings_data_set.csv file.…This rating has one row for each individual movie review.…
To turn this into a matrix that summarizes…all reviews across all movies,…we need to use Pandas pivot table function.…A pivot table takes a list of data…and summarizes it with one row and one column…for each unique user and unique movie in our dataset.…If you have used pivot tables in spreadsheet software…like Microsoft Excel, it works exactly the same way here.…First we pass in the data frame…containing the data we want to summarize.…
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: Represent product reviews as a matrix