Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own!
Java Programming: Build a Recommendation System
Java Programming: Build a Recommendation System
This course is part of Java Programming and Software Engineering Fundamentals Specialization
Instructors: Robert Duvall
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4.7
(639 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 4 hours
Learn at your own pace
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Shareable certificate
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Assessments
4 assignments
Taught in English
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This course is part of the Java Programming and Software Engineering Fundamentals Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
You will start out the capstone project by taking a look at the features of a recommender engine. Then you will choose how to read in and organize user, ratings, and movie data in your program. The programming exercise will provide a check on your progress before moving on to the next step.
What's included
2 videos2 readings1 assignment
Your second step in building a recommender will focus on making simple recommendations based on the average ratings that a movie receives. You'll also make sure that each recommended movie has a least a minimal number of user ratings before including it in your recommendations. Throughout this step you are encouraged you use your knowledge of the seven step process to design useful algorithms and successful programs to solve the challenges you will face.
What's included
1 video2 readings1 assignment
In your third step, you will be encouraged to use interfaces to rewrite your existing code, making it more flexible and more efficient. You will also add filters to select a desired subset of movies that you want to recommend, such as 'all movies under two hours long' or 'all movies made in 2012'. You'll also make your recommendation engine more efficient as you practice software design principles such as refactoring.
What's included
1 video2 readings1 assignment
In your fourth step, you will complete your recommendation engine by finding users in the database that have similar ratings and weighting their input to provide a more personal recommendation for the users of your program. Once you complete this step, you could request ratings of movies from those you know, run your program, and give them recommendations tailored to their own interests and tastes!
What's included
1 video2 readings1 assignment1 peer review
Congratulations on completing your recommender programming project! As we conclude this capstone course, our instructors have a few parting words as you embark in future learning and work in computer science!
What's included
1 video
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4.7 (77 ratings)Offered by
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No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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