关于此课程： 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.

此课程适用人群： 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!

制作方： Duke University

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

基本信息	课程 5（共 5 门，Java Programming and Software Engineering Fundamentals Specialization ）
级别	中级
承诺学习时间	4 weeks of study, 3-6 hours/week
语言	English
如何通过	通过所有计分作业以完成课程。
用户评分	4.7 星平均用户评分 4.7查看学生的留言

授课大纲

第 1 周

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 个视频, 3 个阅读材料

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

已评分: Step One

第 2 周

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 个视频, 3 个阅读材料

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

已评分: Step Two

第 3 周

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 个视频, 3 个阅读材料

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

已评分: Step Three

第 4 周

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 个视频, 3 个阅读材料

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

已评分: Step Four

已评分: 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 个视频

常见问题解答

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运作方式

Coursework

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制作方

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.

价格

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评分和审阅

已评分 4.7，总共 5 个 244 评分

Very interesting project!

good course and very interesting things to learn.

Thanks to all the teachers!!

Enjoyed it!

Java 程序设计：DIY 版本的 Netflix 和亚马逊推荐系统引擎

Java 程序设计：DIY 版本的 Netflix 和亚马逊推荐系统引擎