|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/java-programming-recommender
课程评论：没有评论
课程名称：Java编程：构建推荐系统 课程概述：你是否想过Netflix如何决定推荐哪些电影给你？或者亚马逊如何推荐书籍？通过构建我们自己的简化推荐系统，我们可以更好地理解其工作原理！在这个顶点项目中，你将展示你的问题解决能力和Java编程技巧，通过创建推荐系统来完成这一目标。你将处理电影相关的数据，包括评分，但所涉及的原理同样可以轻松应用于书籍、餐厅等其他领域。你将编写程序来回答关于数据的问题，包括基于用户对多个电影的评分，为用户推荐哪些项目。 具体内容包括： 1. 读取并解析数据到列表和映射中； 2. 计算平均评分； 3. 计算特定评分者与其他用户的相似度； 4. 基于评分向特定用户推荐电影； 5. 在网页上显示推荐的电影。 课程大纲： 1. **引入推荐系统**：你将开始顶点项目，了解推荐引擎的特点，然后选择如何在程序中读取和组织用户、评分和电影数据。编程练习将检验你的进展。 2. **简单推荐**：在构建推荐系统的第二步中，你将专注于基于电影获得的平均评分进行简单推荐。同时确保每部推荐的电影至少有一定数量的用户评分。在这一步中，鼓励你运用七步过程的知识来设计有用的算法和成功的程序，以解决所面临的挑战。 3. **接口、过滤器、数据库**：在第三步中，你将被鼓励使用接口来重写现有代码，使其更加灵活和高效。此外，你还将添加过滤器，以选择想要推荐的电影子集，例如“所有时长少于两小时的电影”或“2012年制作的所有电影”。你还将通过实践软件设计原则（如重构）来提高推荐引擎的效率。 4. **加权平均**：在第四步中，你将通过发现数据库中评分相似的用户，并加权他们的输入，为你的程序用户提供更个性化的推荐来完成推荐引擎的构建。完成这一步骤后，你可以请求你认识的人对电影进行评分，运行程序，并为他们提供符合个人兴趣和品味的推荐！ 5. **告别**：恭喜你完成了推荐编程项目！在结束这个顶点课程时，授课老师将提供一些告别寄语，祝你在未来的计算机科学学习和工作中一切顺利！
Name:Introducing the Recommender
Description: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.
Name:Simple Recommendations
Description: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.
Name:Interfaces, Filters, Database
Description: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.
Name:Weighted Averages
Description: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!
Name:Farewell
Description: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!
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.
