使用Spring Boot的Bootiful微服务 这个示例展示了如何使用Spring Boot创建微服务架构以及如何使用Angular UI显示其数据。 请阅读向您展示如何构建此应用程序的教程。 先决条件： 和 具有身份验证和用户管理API，可通过即时，可扩展的用户基础结构缩短开发时间。 Okta直观的API和专家支持使开发人员可以轻松地验证，管理和保护任何应用程序中的用户和角色。 入门 要安装此示例应用程序，请运行以下命令： git clone https://github.com/oktadeveloper/spring-boot-microservices-example.git cd spring-boot-microservices-example 这将获得本地安装的项目的副本。 要运行客户端和所有服务器，请执行./run.sh或手动执行的。 r= ` pwd ` echo $r # Eureka cd $r /eureka-service echo " Starting Eureka Service... " mvn -q clean spring-boot:

netflix-prize-data 数据集 Netflix数据集包含了1999.12.31-2005.12.31期间匿名客户提供的超过一亿部电影平级

springcloud高版本的整合：包括eureka注册中心、feign、zuul、ribbon、hystrix、turbine监控，如有需要后续上传sleuth+elk、config服务集群、admin等demo，这些资源都是我亲自编写，绝对可用

netflixprize资料

netflix-prize-svd

( Netflix Prize中的协同过滤算法.zip ) 个人收集，仅用学习使用，不可用于商业用途，如有版权问题，请联系删除！

著名的Netflix 智能推荐 百万美金大奖赛使用是数据集. 因为竞赛关闭, Netflix官网上已无法下载. Netflix provided a training data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Each training rating is a quadruplet of the form . The user and movie fields are integer IDs, while grades are from 1 to 5 (integral) stars.[3] The qualifying data set contains over 2,817,131 triplets of the form , with grades known only to the jury. A participating team's algorithm must predict grades on the entire qualifying set, but they are only informed of the score for half of the data, the quiz set of 1,408,342 ratings. The other half is the test set of 1,408,789, and performance on this is used by the jury to determine potential prize winners. Only the judges know which ratings are in the quiz set, and which are in the test set—this arrangement is intended to make it difficult to hill climb on the test set. Submitted predictions are scored against the true grades in terms of root mean squared error (RMSE), and the goal is to reduce this error as much as possible. Note that while the actual grades are integers in the range 1 to 5, submitted predictions need not be. Netflix also identified a probe subset of 1,408,395 ratings within the training data set. The probe, quiz, and test data sets were chosen to have similar statistical properties. In summary, the data used in the Netflix Prize looks as follows: Training set (99,072,112 ratings not including the probe set, 100,480,507 including the probe set) Probe set (1,408,395 ratings) Qualifying set (2,817,131 ratings) consisting of: Test set (1,408,789 ratings), used to determine winners Quiz set (1,408,342 ratings), used to calculate leaderboard scores For each movie, title and year of release are provided in a separate dataset. No information at all is provided about users. In order to protect the privacy of customers, "some of the rating data for some customers in the training and qualifyin