Details
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Sub-task
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Status: Closed
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Major
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Resolution: Duplicate
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Description
Currently mllib.recommendation.MatrixFactorizationModel has methods recommendProducts/recommendUsers for recommending top K to a given user / item, as well as recommendProductsForUsers/recommendUsersForProducts to recommend top K across all users/items.
Additionally, SPARK-10802 is for adding the ability to do recommendProductsForUsers for a subset of users (or vice versa).
Look at exposing or porting (as appropriate) these methods to ALS in ML.
Investigate if efficiency can be improved at the same time (see SPARK-11968).
Attachments
Issue Links
- is duplicated by
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SPARK-14379 Review spark.ml parity for recommendation
- Closed
- is related to
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SPARK-19535 ALSModel recommendAll analogs
- Resolved
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SPARK-14412 spark.ml ALS prefered storage level Params
- Resolved
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SPARK-11968 ALS recommend all methods spend most of time in GC
- Resolved
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SPARK-18235 ml.ALSModel function parity: ALSModel should support recommendforAll
- Resolved
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SPARK-10802 Let ALS recommend for subset of data
- Resolved
- relates to
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SPARK-14489 RegressionEvaluator returns NaN for ALS in Spark ml
- Resolved
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SPARK-14409 Investigate adding a RankingEvaluator to ML
- Resolved
- links to