課程大綱：Java編程：構(gòu)建推薦系統(tǒng)培訓(xùn)
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 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.
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.
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!
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!
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