Java Programming: Build a Recommendation System

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 Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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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 expand

1. Reading: Module Description / Resources
2. Video: Introduction and Motivation
3. Video: Reading and Storing Data
4. Reading: Programming Exercise: Step One
5. 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 expand

1. Reading: Module Description
2. Video: Average Ratings
3. Reading: Programming Exercise: Step Two
4. 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 expand

1. Reading: Module Description
2. Video: Filtering Recomendations
3. Reading: Programming Exercise: Step Three
4. 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 expand

1. Reading: Module Description
2. Video: Calculating Weighted Averages
3. Reading: Programming Exercise: Step Four
4. 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 expand

1. Video: Farewell from the Instructor Team