Java Programming: Build a Recommendation System from Coursera

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.

What's inside

Syllabus

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.

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.

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.

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!

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!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops data literacy and programming skills, which are relevant in many fields

Provides hands-on practice with building recommender systems

Taught by instructors recognized for their work in computer science education

Builds a strong foundation for beginners in recommender system development

Requires students to have some prior programming knowledge

May require additional resources or software to complete the course

Reviews summary

Engaging introductory course to java

According to students, this course offers an engaging introduction to Java programming. Students point to good content and an interesting curriculum that makes learning the basics fun.

This course provides interesting content.

"good course"

"very interesting things to learn"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Java Programming: Build a Recommendation System with these activities:

Read a book about algorithms

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Absorb new or review existing knowledge of algorithms before the course begins.

View The Algorithm Design Manual (Texts in Computer... on Amazon

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Choose a book.
Set aside time to read the book.
Take notes while reading.
Complete any exercises or problems in the book.

Watch a tutorial on Python

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Learn foundational Python concepts by following a structured video tutorial playlist.

Browse courses on Python

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Search for a Python tutorial.
Watch the tutorial.

Volunteer at a local library or museum

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Share your knowledge and skills while supporting your community and expanding your network.

Browse courses on Communication

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Contact the library or museum to inquire about volunteer opportunities.
Attend a volunteer orientation.
Complete volunteer training.

20 other activities

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Review Java Programming

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Refreshes essential Java programming skills necessary for this course.

Browse courses on Java Programming

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Review Python data structures and algorithms

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Reinforce understanding of data structures and algorithms used in recommender systems.

Browse courses on Data Structures

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Review online tutorials on Python data structures and algorithms.
Solve coding problems related to data structures and algorithms.

Explore the Netflix Prize dataset

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Familiarize yourself with real-world data used in recommender systems research.

Browse courses on Recommender Systems

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Download the Netflix Prize dataset.
Explore the data and identify key features.

Practice math with crosswords

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Execute math operations by solving short puzzles to refresh and retain skills and knowledge.

Browse courses on Algebra

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Download the crossword puzzle book.
Look over the puzzle and look up the answers to any clues you don't know.
Work the crossword puzzle.

Form a study group

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Engage with fellow learners, share knowledge, and collectively overcome challenges.

Browse courses on Collaboration

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Find a group of classmates to study with.
Set up a regular meeting time and place.
Plan out the topics you will cover in each meeting.
Meet regularly to discuss the course material.

Join a study group or participate in online discussions

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Joining a study group or participating in online discussions will help you learn from others and get help with difficult concepts.

Browse courses on Recommender Systems

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Find a study group or online discussion forum.
Introduce yourself and ask questions.
Help others with their questions.

Build a simple calculator

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Apply Python skills by building a practical tool to reinforce your understanding.

Browse courses on Programming

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Design the calculator.
Write the code for the calculator.
Test the calculator.

Practice Implementing Recommendation Algorithms

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Provides hands-on practice in implementing recommendation algorithms, reinforcing concepts learned in this course.

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Choose a recommendation algorithm to implement, such as user-based collaborative filtering or item-based collaborative filtering.
Gather and prepare data for the chosen algorithm.
Implement the algorithm using your preferred programming language.
Evaluate the performance of your implemented algorithm.

Write code for movie recommendation engine

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Follow a step-by-step guide to code a simple movie recommendation engine.

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Install and set up a Python environment.
Import necessary libraries.
Load and parse movie and user data.
Calculate movie ratings.
Generate movie recommendations.

Generate a report on recommender systems' case study

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Create a report on a case study of a recommender system to understand how data is used to make recommendations.

Browse courses on Recommender Systems

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Research different recommender system case studies.
Select a case study and gather relevant data.
Analyze the data and identify key patterns and trends.
Write a report summarizing your findings and insights.

Solve coding challenges on LeetCode or HackerRank

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Enhance your problem-solving skills and coding abilities.

Browse courses on Algorithms

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Select a coding challenge that aligns with course concepts.
Implement the solution using the appropriate algorithms and data structures.

Solve practice problems on Java and data structures

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Solving practice problems will help you improve your programming skills and understanding of data structures and algorithms, which are essential for this course.

Browse courses on Java

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Find a set of practice problems online or in a book.
Choose a problem to solve.
Break the problem down into smaller steps.
Write code to solve the problem.
Test your code to make sure it works.

Solve LeetCode problems

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Practice problem-solving by tackling coding challenges regularly.

Browse courses on Algorithms

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Sign up for a LeetCode account.
Choose a problem to solve.
Write the code to solve the problem.
Submit your solution.

Implement '3-movies' problem

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Practice implementing code for the '3-movies' problem to develop skills in working with recommendation engines.

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Define the '3-movies' problem.
Design an algorithm to solve the problem.
Implement the algorithm in code.

Follow tutorials on building recommender systems

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Following tutorials on building recommender systems will help you understand how they work and how to implement them yourself.

Browse courses on Recommender Systems

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Find a tutorial on building recommender systems.
Follow the steps in the tutorial to build a recommender system.
Test your recommender system to see how well it works.
Optionally: Modify the recommender system to improve its performance.

Write a blog post about a topic related to the course

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Demonstrate and deepen your understanding by explaining a relevant concept or applying a technique.

Browse courses on Recommender Systems

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Choose a topic.
Research the topic.
Write the blog post.
Publish the blog post.

Develop a Personalized Recommendation System

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Allows you to apply the concepts learned in this course to create a tailored recommendation system for a specific domain, deepening your understanding.

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Define the domain and gather relevant data.
Choose and implement suitable recommendation algorithms.
Design and develop a user interface for the recommendation system.
Evaluate the performance and user experience of the system.

Build web user interface for recommendation engine

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Create a user-friendly interface for the movie recommendation engine.

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Design the user interface.
Build the front-end using HTML and CSS.
Implement the back-end using Python and Flask.
Test and deploy the web application.

Contribute to an open-source recommender system

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Gain practical experience by contributing to a real-world recommender system.

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Identify an area to contribute to.
Identify an open-source recommender system project.
Read the project documentation.
Implement your contribution.

Deploy recommendation engine as a RESTful API

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Make the recommendation engine accessible to other applications through a RESTful API.

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Design the API.
Implement the API using Python and Flask.
Test and deploy the API.
Document the API for developers.

Career center

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Project Manager

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Data Scientist

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Machine Learning Engineer

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Data Engineer

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