[SPARK-10802] Let ALS recommend for subset of data

• Type: Improvement
• Status: Resolved
• Priority: Minor
• Resolution: Won't Fix
• Affects Version/s: 1.5.0
• Component/s: MLlib
• Assignee: Unassigned
• Reporter: Tomasz Bartczak
• Created: 24/Sep/15 16:07
• Updated: 09/Oct/17 08:44
• Resolved: 09/Oct/17 08:44

Description

Currently MatrixFactorizationModel allows to get recommendations for

• single user
• single product
• all users
• all products

recommendation for all users/products do a cartesian join inside.

It would be useful in some cases to get recommendations for subset of users/products by providing an RDD with which MatrixFactorizationModel could do an intersection before doing a cartesian join. This would make it much faster in situation where recommendations are needed only for subset of users/products, and when the subset is still too large to make it feasible to recommend one-by-one.

Issue Links

• is duplicated by: SPARK-15504 Could MatrixFactorizationModel support recommend for some users only ?
• is related to: SPARK-19535 ALSModel recommendAll analogs
• is related to: SPARK-11968 ALS recommend all methods spend most of time in GC
• is related to: SPARK-20679 Let ML ALS recommend for a subset of users/items
• relates to: SPARK-13857 Feature parity for ALS ML with MLLIB