spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Yuhao Yang <hhb...@gmail.com>
Subject Re: Re: how to call recommend method from ml.recommendation.ALS
Date Thu, 16 Mar 2017 05:05:30 GMT
This is something that was just added to ML and will probably be released
with 2.2. For now you can try to copy from the master code:
https://github.com/apache/spark/blob/70f9d7f71c63d2b1fdfed75cb7a59285c272a62b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala#L352
and give it a try.

Yuhao

2017-03-15 21:39 GMT-07:00 lk_spark <lk_spark@163.com>:

> thanks for your reply , what I exactly want to know is :
> in package mllib.recommendation  , MatrixFactorizationModel have method
> like recommendProducts , but I didn't find it in package ml.recommendation.
> how can I do the samething as mllib when I use ml.
> 2017-03-16
> ------------------------------
> lk_spark
> ------------------------------
>
> *发件人:*任弘迪 <ryan.hd.ren@gmail.com>
> *发送时间:*2017-03-16 10:46
> *主题:*Re: how to call recommend method from ml.recommendation.ALS
> *收件人:*"lk_spark"<lk_spark@163.com>
> *抄送:*"user.spark"<user@spark.apache.org>
>
> if the num of user-item pairs to predict aren't too large, say millions,
> you could transform the target dataframe and save the result to a hive
> table, then build cache based on that table for online services.
>
> if it's not the case(such as billions of user item pairs to predict), you
> have to start a service with the model loaded, send user to the service,
> first match several hundreds of items from all items available which could
> itself be another service or cache, then transform this user and all items
> using the model to get prediction, and return items ordered by prediction.
>
> On Thu, Mar 16, 2017 at 9:32 AM, lk_spark <lk_spark@163.com> wrote:
>
>> hi,all:
>>        under spark2.0 ,I wonder to know after trained a
>> ml.recommendation.ALSModel how I can do the recommend action?
>>
>>        I try to save the model and load it by MatrixFactorizationModel
>> but got error.
>>
>> 2017-03-16
>> ------------------------------
>> lk_spark
>>
>
>

Mime
View raw message