Java Programming: Build a Recommendation System
Create a Netflix-style movie recommendation engine using Java. Learn data structures, interfaces, and algorithmic thinking.
Create a Netflix-style movie recommendation engine using Java. Learn data structures, interfaces, and algorithmic thinking.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Java Programming and Software Engineering Fundamentals Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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বাংলা, اردو, Tiếng Việt, 2 more
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What you'll learn
Parse and organize movie ratings data
Implement average rating calculations
Create user similarity algorithms
Build weighted recommendation systems
Display recommendations through web interface
Skills you'll gain
This course includes:
0.6 Hours PreRecorded video
4 quizzes, 1 peer review
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This capstone course teaches students how to build a movie recommendation system similar to those used by Netflix and Amazon. The curriculum covers data parsing, average rating calculations, user similarity metrics, and weighted recommendations. Students implement the system step-by-step, from basic functionality to advanced features using Java, while learning practical software design principles and data structure manipulation.
Introducing the Recommender
Module 1 · 1 Hours to complete
Simple Recommendations
Module 2 · 52 Minutes to complete
Interfaces, Filters, Database
Module 3 · 58 Minutes to complete
Weighted Averages
Module 4 · 2 Hours to complete
Farewell
Module 5 · 1 Minutes to complete
Instructors
Associate Professor of the Practice
Andrew Hilton is an Associate Professor of the Practice in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where he has been teaching since 2012. Before joining Duke, he worked as an advisory engineer at IBM. One of the key courses he teaches is ECE 551, an intensive introduction to programming designed to equip graduate students with no prior experience to master programming and tackle advanced courses. In 2015, Professor Hilton received the Klein Family Distinguished Teaching Award for his excellence in teaching. He holds a Ph.D. in Computer Science from the University of Pennsylvania.
Professor of the Practice in Computer Science at Duke University
Susan H. Rodger is a Professor of the Practice in the Computer Science Department at Duke University, where she specializes in visualization, interaction, and computer science education. She earned her PhD and M.S. in Computer Science from Purdue University and her B.S. in Computer Science and Mathematics from North Carolina State University. Professor Rodger is renowned for developing JFLAP, an educational software tool widely used for teaching formal languages and automata theory, which has been implemented globally in over 160 countries. She also leads the Adventures in Alice Programming project, which integrates computing into K-12 education by providing curriculum materials and professional development for teachers. Her significant contributions to computer science education have been recognized with numerous awards, including the 2013 ACM Karl V. Karlstrom Outstanding Educator Award and the 2019 Taylor L. Booth Education Award. In addition to her teaching and research, she has authored two books and published over fifty journal and conference articles. Outside of academia, Susan enjoys reading, hiking, traveling, swimming, and baking, often creating computer science-themed cookies for her students.
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