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
20. None

21. Why not deep learning?

22. Code, Slides and Contact ____ Code will be open-sourced soon!

    Harjinder Mistry email:hmistry@redhat.com