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https://issues.apache.org/jira/browse/SPARK-27447?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-27447:
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Affects Version/s: (was: 2.5.0)
3.0.0
> Add collaborate filtering Explain API in SPARKML
> ------------------------------------------------
>
> Key: SPARK-27447
> URL: https://issues.apache.org/jira/browse/SPARK-27447
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Affects Versions: 3.0.0
> Reporter: guohao xiao
> Priority: Minor
>
> Machine learning recommender systems have supercharged the online retail
> environment by directly targeting what the customer wants. While customers
> are getting better product recommendations than ever before, in the age of
> GDPR there is growing concern about customer privacy and transparency with ML
> models. Many are asking, just why am I receiving these recommendations? While
> the current Implicit Collaborative Filtering (CF) algorithm in spark.ml is
> great for generating recommendations at scale, its currently lacks any method
> to explain why a particular customer is getting the recommendations they are
> getting. In this talk, we demonstrate a way to expand collaborative filtering
> so that the viewing history of a customer can be directly related to their
> recommendations. Why were you recommended footwear? Well, 40% of this
> recommendation came from browsing runners and 20% came from the shorts you
> recently purchased. Turns out, rethinking of the linear algebra in the
> current spark.ml CF implementation makes this possible. We show how this is
> done and demonstrate its implemented as a new feature to spark.ml, expanding
> the API to allow everyone to explain recommendations at scale and create a
> more transparent ML future.
>
>
> This project is going to present in Spark summit 2019:
> https://databricks.com/sparkaisummit/north-america/sessions-single-2019?id=56
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