推荐系统企业工程实践项目是大数据基础应用的延伸。 它建立在用户画像项目之上。 旨在让学生学习企业级推荐系统构建的基本思路，深入讲解推荐系统中最重要的两个环节。 Recall and Sorting，每个环节都引入了基于Spark-Mllib的相关算法，如召回层ItemCF、ALS双向召回算法，融合排序层引入GBDT+LR。 在理解算法的同时，更注重工程实践。 我们将从原始数据的特征提取、转换、算法模型设计到编程实现进行深入讲解，同时给出算法模型跨平台部署方案的实际案例，让学生 可以学习算法模型在实际项目中是如何部署和使用的。
(The recommendation system enterprise engineering practice project is an extension of the basic application of big data. It is based on the user portrait project. The purpose is to let students learn the basic idea of the construction of enterprise level recommendation system and explain the two most important links in the recommendation system. For recall and sorting, relevant algorithms based on spark mlib are introduced in each link, such as itemcf and ALS bidirectional recall algorithms in the recall layer, and gbdt + LR is introduced in the fusion sorting layer. While understanding the algorithm, we should pay more attention to engineering practice. We will give an in-depth explanation from the feature extraction and transformation of the original data, the design of the algorithm model to the programming implementation, and give the actual case of the cross platform deployment scheme of the algorithm model, so that students can learn how to deploy and use the algorithm model in the actual project.)
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