À propos de ce cours : 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.

Les destinataires de ce cours : 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!

Créé par : Duke University

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

Informations de base	Cours 5 de 5 dans Java Programming and Software Engineering Fundamentals Specialization
Niveau	Intermediate
Engagement	4 weeks of study, 3-6 hours/week
Langue	English
Comment réussir	Réussissez tous les devoirs notés pour terminer le cours.
Notes des utilisateurs	4.7 étoiles Note moyenne des utilisateurs 4.7Voir ce que disent les étudiants

Programme de cours

SEMAINE 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 vidéos, 3 lectures

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

Noté: Step One

SEMAINE 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 vidéo, 3 lectures

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

Noté: Step Two

SEMAINE 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 vidéo, 3 lectures

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

Noté: Step Three

SEMAINE 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 vidéo, 3 lectures

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

Noté: Step Four

Noté: 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 vidéo

FAQ

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Comment cela fonctionne

Trabajo del curso

Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.

Ayuda de tus compañeros

Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.

Certificados

Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.

Créateurs

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.

Tarification

	Acheter le cours
Accéder au contenu du cours	Disponible
Accéder au contenu noté	Disponible
Recevoir une Note Finale	Disponible
Obtenir un Certificat de Cours partageable	Disponible

Notation et examens

Note moyenne 4.7 sur 5 sur 221 notes

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