如何处理模型由MLlib星火ALS产生了新的用户/项目？ [英] How can I handle new users/items in model generated by Spark ALS from MLlib?

问题描述

目前，当一个新的用户来了，我不能更新其AP prently就是没有添加的用户和项目矩阵相关的我的推荐系统。
我在哪里可以找到这个问题以及如何做到这一点？谢谢

currently when a new user comes I cannot update my recommender system which apprently is related to not having added the user and item matrix. Where can I find this and how to do this? Thanks

model.userFactors
model.itemFactors

model.userFactors model.itemFactors

推荐答案

在项目的功能和用户特征的计算模型只是ppared推荐已知的项目和用户$ P $。如果您有新的用户/项目，你必须应付的冷启动的问题。

When items features and users features are computed the model is prepared only to recommend for known items and users. If You have new user/item, You have to cope with cold start problem.

但有两件事情 - 提出建议，对新用户/项目工作和独立的东西被更新模型（功能矩阵）近线。

But there are two things - making recommendations work for new users/items and the separate thing is updating the model (features matrices) near-online.

在为新的/匿名用户prepare的建议，这是不是在输入数据时模式是身材，你必须prepare它的功能载体。该方法是从项目的特点prepare它已经看到（或任何形式的互动你正在考虑为'像'），例如从哪些用户喜欢这些项目每一个特征计算平均值。或羚羊code查找的<一个方法href=\"https://github.com/cloudera/oryx/blob/master/als-serving/src/main/java/com/cloudera/oryx/als/serving/ServerRecommender.java#L392\"相对=nofollow>建筑匿名用户特征向量

In order to prepare recommendations for new/anonymous user, which wasn't in input data when model was build, You have to prepare it's features vector. The method is to prepare it from features of items already seen (or any kind of interaction You are considering as 'like'), e.g. calculate mean value for every feature from those items which user liked. Or look at Oryx code for the method of building anonymous user feature vector

有关使用折叠式的技术，例如更新模型近线（我写的近，因为面对它，真正的在线更新是不可能的）：
<一href=\"http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2008-Online_Updating_Regularized_Kernel_Matrix_Factorization_Models.pdf\"相对=nofollow>在线更新正则内核矩阵分解模型为大规模推荐系统。
OU你可以看看code：

For updating Your model near-online (I write near, because face it, the true online update is impossible) by using fold-in technique, e.g.: Online-Updating Regularized Kernel Matrix Factorization Models for Large-Scale Recommender Systems. Ou You can look at code of:

• MyMediaLite


• 羚羊 - 框架构建和λ建筑范例。它应该有新的用户/项目折叠的更新。

• MyMediaLite
• Oryx - framework build with Lambda Architecture paradigm. And it should have updates with fold-in of new users/items.

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