movie-recommender-demo:该项目逐步介绍了如何使用Apache Spark机器学习创建推荐。 您可以在IBM Data Science Experience上运行许多jupyter笔记本,并且可以与电影推荐Web应用程序进行实时演示。 该演示还使用IBM Message Hub(kafka)将应用程序事件推送到主题,在IBM BigInsights(hadoop)上运行的Spark Streaming作业将这些事件消耗掉-源码

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总览 该项目介绍了如何使用Apache Spark机器学习创建建议。 您可以在IBM Data Science Experience上运行许多jupyter笔记本,并且可以与电影推荐Web应用程序进行实时演示。 该演示还使用IBM Message Hub(kafka)将应用程序事件推送到主题,在IBM BigInsights(hadoop)上运行的Spark Streaming作业将其消费。 快速开始 如果您想尝试该Web应用程序的实时演示,请访问。 这也是的概述视频。 该项目是演示电影推荐器应用程序。 该演示已安装了约4000部电影和500,000个评级。 评级是随机生成的。 此Web应用程序的目的是允许用户搜索电影,对电影进行评级并根据其评级接收电影的推荐。 笔记本电脑 从开始,以了解有关该项目的更多信息。 您可以将这些笔记本导入IBM Data Science Experience。 尝试从URL加载时,偶尔会遇到问题。 如果发生这种情况,请尝试克隆或下载此存储库,然后将笔记本作为文件导入。 技术领域 总体架构如下所示: 本演示中使用的技术是: 核心组件(Web应用程序)

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