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Recommendation Engine for wide transactions
harjinder-hari
June 09, 2017
Programming
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Recommendation Engine for wide transactions
harjinder-hari
June 09, 2017
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Transcript
Rec Sys - wide transactions Harjinder Mistry Red Hat |
@hmistry
Agenda 1. RecSys - 2 min primer 2. Problem -
Definition 3. Challenges in Standard Approaches 4. Our approach & architecture
RecSys examples
Basic terminologies user-item matrix explicit vs implicit feedback — user-user
— user-item — item-item image source
Frequent Pa!ern mining Applications — Customer Analysis — Brick-and-mortar retail
— Handling cold-start situation — Retrieval
Frequent Pa!ern mining Algorithms — apriori — FP Growth
openshi!.io
Helping developers become more efficient recommendations on packages recommendations on
the stack
Input data Projects/stacks - from code repositories — Java (pom.xml)
— Node.js (packages.json) — Python (requirements.txt)
spark, elastic cloud compute.... cool - let's rock
developers are amazing - but, of course
Wide transactions - challenges — existing methods didn't work —
time to train was huge — memory issues
As a self-serve platform, turnaround time as important as accuracy
Matrix Factorization is fast image source
Let's use matrix factorization (ALS) to generate frequent pa!erns
Step 1: Train ALS model
Step 2: Generate initial seed: random candidate set
Step 3: Find recommended product(package)
Step 4: Add it to the frequent pa!ern list and
continue
None
Why not deep learning?
Code, Slides and Contact ____ Code will be open-sourced soon!
Harjinder Mistry email:hmistry@redhat.com