如何增加Spark ALS推荐器中的矩阵因子? [英] How to augment matrix factors in Spark ALS recommender?

问题描述

我是机器学习和Apache Spark用法的初学者.
我已经按照 https://databricks-training的指南进行操作. s3.amazonaws.com/movie-recommendation-with-mllib.html#augmenting-matrix-factors ，并成功开发了该应用程序.现在，由于需要通过实时建议来支持当今的Web应用程序，因此我希望我的模型能够为不断出现在服务器上的新数据做好准备. 该网站引用了以下内容:

I am a beginner to the world of Machine Learning and the usage of Apache Spark.
I have followed the tutorial at https://databricks-training.s3.amazonaws.com/movie-recommendation-with-mllib.html#augmenting-matrix-factors, and was succesfully able to develop the application. Now, as it is required that today's web application need to be powered by real time recommendations, I would like my model to be ready for new data that keeps coming on the server. The site has quoted:

更好地为您提供建议的方法是先训练矩阵分解模型，然后使用您的评分扩充模型.

A better way to get the recommendations for you is training a matrix factorization model first and then augmenting the model using your ratings.

我该怎么做?我正在使用Python开发应用程序. 另外，请告诉我如何持久保存模型以再次使用它，或者告诉我如何将其与Web服务接口. 谢谢你

How do I do that? I am using Python to develop my application. Also, please tell me how do I persist the model to use it again, or an idea how do I interface this with a web service. Thanking you

推荐答案

我认为Spark中的ALS无法在线学习.这意味着您无法在实时获取数据的同时更新模型.但是，您可以使用该模型来获取预测.

I don't think online learning is possible for ALS in Spark. That means you can't update the model while getting the data in real time. However, you can use the model to get the predictions.

另外，请参考: 如何为ALS更新Spark MatrixFactorizationModel

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