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\nLet’s Make A Movie Recommendation System (LAB)\n

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\n\nPublisher:\n\nCrash Course\n\n\n
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\nWe need to save Jabril and John Green Bot’s movie nights. Jabril generally likes action movies and John Green Bot likes romantic movies, but they need to find something that they can both watch and enjoy together. Today, we’re going to...\nWe need to save Jabril and John Green Bot’s movie nights. Jabril generally likes action movies and John Green Bot likes romantic movies, but they need to find something that they can both watch and enjoy together. Today, we’re going to build a movie recommender system to find that perfect movie. With the help of the LensKit library, our AI will use existing movie ratings from the MovieLens dataset and personalized ratings from Jabril and John Green Bot to perform user-user collaborative filtering. We’ll then create a Jabril Green Bot hybrid that will average these ratings to try and find something that they both want to watch. \n
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Resource Details

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Curator Rating
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Educator Rating
Not yet Rated
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Media Length
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14:33
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Grade
12th - Higher Ed
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Subjects
Social Studies & History
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Media Type
Instructional Videos
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Source:
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\nCrash Course Artificial Intelligence\n\n
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Date
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2019
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Additional Materials

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Concepts

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data, john green, artificial intelligence, computer science
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Additional Tags

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rated, robot, lists, build, make predictions, print, list, collaborative filtering, jibril green, bigger datasets, average, movie night, recommender system, rating, number, reasonable, lab, recommendations, lenskit library, sorted list, machine learning, movie ratings, average rating, movielens, data set, thing, generic recommendations, princess bride, perfect 5, make, recommend, idea, ai movie recommender system, movie, button, crash course, rated movies
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Classroom Considerations

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Best For: Explaining a topic
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Educator Ratings

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