[
https://issues.apache.org/jira/browse/BIGTOP-1537?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
RJ Nowling updated BIGTOP-1537:
-------------------------------
Component/s: blueprints
> Add BigPetStore Spark Item Recommender example
> ----------------------------------------------
>
> Key: BIGTOP-1537
> URL: https://issues.apache.org/jira/browse/BIGTOP-1537
> Project: Bigtop
> Issue Type: Improvement
> Components: blueprints
> Reporter: RJ Nowling
>
> We should add an example for using Spark and MLlib to build an item
> recommender.
> Two challenges:
> 1. The data generator does not generate user product ratings. We need a way
> to provide a metric for the "strength" of an interaction between a user and
> product. This could be the normalized purchase frequency for each product.
> Further evaluation is needed.
> 2. How to evaluate the recommendations. We will want to divide the user
> data into 2 groups: validation and training. For the validation group, we
> may want to drop certain products and see if the recommender fills in those
> products or something similar.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
