Acerca de este curso: 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.

Para quién es esta clase: 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!

Creada por: Duke University

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

Información básica	Curso 5 de 5 en Java Programming and Software Engineering Fundamentals Specialization
Nivel	Intermediate
Compromiso	4 weeks of study, 3-6 hours/week
Idioma	English
Cómo aprobar	Aprueba todas las tareas calificadas para completar el curso.
Calificaciones del usuario	4.7 estrellas Calificación promedio del usuario 4.7Ve los que los estudiantes dijeron

Programa

SEMANA 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
Vídeo: Introduction and Motivation
Vídeo: Reading and Storing Data
Reading: Programming Exercise: Step One
Reading: End of Module Survey

Calificado: Step One

SEMANA 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
Vídeo: Average Ratings
Reading: Programming Exercise: Step Two
Reading: End of Module Survey

Calificado: Step Two

SEMANA 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
Vídeo: Filtering Recomendations
Reading: Programming Exercise: Step Three
Reading: End of Module Survey

Calificado: Step Three

SEMANA 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
Vídeo: Calculating Weighted Averages
Reading: Programming Exercise: Step Four
Reading: End of Module Survey

Calificado: Step Four

Calificado: 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

Vídeo: Farewell from the Instructor Team

Preguntas Frecuentes

When will I have access to the lectures and assignments?

What will I get if I pay for this course?

What is the refund policy?

Is financial aid available?

Cómo funciona

Coursework

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

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Creadores

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.

Tarifa

	Comprar curso
Accede a los materiales del curso	Disponible
Accede a los materiales con calificación	Disponible
Recibe una calificación final	Disponible
Obtén un Certificado de curso para compartir	Disponible

Calificaciones y revisiones

Calificado 4.7 de 5 215 calificaciones

good course and very interesting things to learn.

Thanks to all the teachers!!

Enjoyed it!

Fantastic course! The instructors were very engaging, the lessons were varied and provided me with a wide variety of information in the field. After completely this course, I definitely feel much more confident working in this field.

Java Programming: Build a Recommendation System

Java Programming: Build a Recommendation System