Recommendation Engine for wide transactions

Transcript

1. Rec Sys - wide transactions Harjinder Mistry Red Hat | @hmistry
2. Agenda 1. RecSys - 2 min primer 2. Problem - Definition 3. Challenges in Standard Approaches 4. Our approach & architecture
3. RecSys examples
4. Basic terminologies user-item matrix explicit vs implicit feedback — user-user — user-item — item-item image source
5. Frequent Pa!ern mining Applications — Customer Analysis — Brick-and-mortar retail — Handling cold-start situation — Retrieval
6. Frequent Pa!ern mining Algorithms — apriori — FP Growth
7. openshi!.io
8. Helping developers become more efficient recommendations on packages recommendations on the stack
9. Input data Projects/stacks - from code repositories — Java (pom.xml) — Node.js (packages.json) — Python (requirements.txt)
10. spark, elastic cloud compute.... cool - let's rock
11. developers are amazing - but, of course
12. Wide transactions - challenges — existing methods didn't work — time to train was huge — memory issues
13. As a self-serve platform, turnaround time as important as accuracy
14. Matrix Factorization is fast image source
15. Let's use matrix factorization (ALS) to generate frequent pa!erns
16. Step 1: Train ALS model
17. Step 2: Generate initial seed: random candidate set
18. Step 3: Find recommended product(package)
19. Step 4: Add it to the frequent pa!ern list and continue
21. Why not deep learning?
22. Code, Slides and Contact ____ Code will be open-sourced soon! Harjinder Mistry email:hmistry@redhat.com