Understanding how to find similar products in a real program
- [Instructor] In the previous video,…we created these four data files.…If you like to create them now,…run train_recommender_cold_start final.py…before you continue.…Now let's open up product_similarity_from_data_files.py.…When you show related products in the real application,…you don't want to have refactor the matrix each time…because it's too slow.…Instead, you can use the product_features.dat file…to calculate product similarity quickly.…First we'll load the product_features.dat file…using Python's pickle.load function.…
The M matrix that we just loaded…has one column for each movie.…Let's transpose the matrix so each column becomes a row.…This just makes the data easier to work with,…but it doesn't change the data.…Next, we'll load the movie list using read_csv…so we'll have access to the movie titles.…And we'll pick a movie to find similar movies to.…I've chosen movie_id = 5.…Next we'll look up this movie in the movies_df dataframe…and then we'll print out the movie's title and genre.…Now we're ready to calculate movie similarity.…
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: Find similar products