About this course: 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! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.

Who is this class for: This course is for anyone who has passed the first four courses in the Java Programming and Software Engineering Fundamentals Specialization, who has the ability to program in Java and design algorithms. Bring together everything you’ve learned to make a movie recommendation system that you can put on the web and send to your colleagues and friends!

Created by: Duke University

Taught by: Robert Duvall, Lecturer
Computer Science
Taught by: Owen Astrachan, Professor of the Practice
Computer Science
Taught by: Andrew D. Hilton, Assistant Professor of the Practice
Electrical and Computer Engineering
Taught by: Susan H. Rodger, Professor of the Practice
Computer Science

Basic Info	Course 5 of 5 in the Java Programming and Software Engineering Fundamentals Specialization
Level	Intermediate
Commitment	4 weeks of study, 3-6 hours/week
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.7 stars Average User Rating 4.7See what learners said

Syllabus

WEEK 1

Introducing the Recommender

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.

2 videos, 3 readings

Reading: Module Description / Resources
Video: Reading and Storing Data
Reading: Programming Exercise: Step One
Reading: End of Module Survey

Graded: Step One

WEEK 2

Simple Recommendations

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.

1 video, 3 readings

Reading: Module Description
Reading: Programming Exercise: Step Two
Reading: End of Module Survey

Graded: Step Two

WEEK 3

Interfaces, Filters, Database

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.

1 video, 3 readings

Reading: Module Description
Reading: Programming Exercise: Step Three
Reading: End of Module Survey

Graded: Step Three

WEEK 4

Weighted Averages

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!

1 video, 3 readings

Reading: Module Description
Reading: Programming Exercise: Step Four
Reading: End of Module Survey

Graded: Step Four

Graded: Step Five

Farewell

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!

1 video

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How It Works

Coursework

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Creators

Duke University

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.

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Rated 4.7 out of 5 of 226 ratings

Java Programming: Build a Recommendation System

Java Programming: Build a Recommendation System