The document discusses Zulily's goal of building personalized recommendation systems to provide a tailored shopping experience across platforms by predicting demand, enabling collaborative filtering, and sorting sales based on a member's likelihood to engage. It describes challenges around rapid growth and short sales windows and their approach of breaking products into features and using member interactions to compute interests to recommend relevant sales. It also covers techniques like contextual multi-armed bandits and real-time experimentation to optimize recommendations and landing page images.

1. PERSONALIZATION
@ ZULILY
PERSONALIZED WEB & MOBILE EXPERIENCE
SaraAdineh

2. WICS @ SFU
WOMEN IN COMPUTING SCIENCE @ SIMON FRASER UNIVERSITY
InternationalWomensDayCelebration2011&2012

3. TRY/CATCH
TRY COMPUTING AND TECHNOLOGY CONFERENCE FOR HER (MAY 2011)

4. OUR GOAL
Buildingrecommendation systems to provide apersonalized, fun
zulilyexperience across allplatforms
Predictdemand atSKU(Stock KeepingUnit) level
Collaborative filteringto increase discovery
Sortsales based on member's likelihood to engage
Displayrightsale eventimages for each member

5. EXAMPLE #1 (TUTUS)

6. EXAMPLE #2 (NO TUTUS)

7. EXAMPLE #3

8. CHALLENGES

9. CHALLENGES
Our business is growingveryfast
Launching~100 new sales events and ~9000
products everyday
Expandinginternationally: Canada, Australia, and UK
Mostof these sales events are onlyfor three days
Rapid customer growth
PROVIDE
Member-specific recommendations
Optimization of the bestshoppingexperience in real time
Scalable, highlyavailable with verylow latencyservices

10. OUR APPROACH
Break productcatalogdown into features
Aneventwithstrongfeaturescoresforkids&girls

11. Use member interactions to compute interestin these features.
OUR APPROACH
Events and products you have clicked on
Products thatyou have added to your cart
Products you have purchased

12. OUR APPROACH
Use member interactions to compute interestin these features

13. OUR APPROACH
Recommend sales based on these interests
Continuouslyre-evaluate recommendations.

14. HOW WE CHOOSE THE IMAGES ON THE
LANDING PAGE?
Differentimages dispalyed on the same sale for each member

15. CONTEXTUAL MULTI-ARMED BANDIT
PROBLEM
Implementingoptimization algorithms thatpick the bestrestult
from aknown, relativelysmallsetof potentialcandidates.
Learnmore@engineering.zulily.com

16. REAL TIME EXPERIMENTATION

17. WE CONTRIBUTE BACK TO OUR COMMUNITY!
We have opensourced some of our projects:
dropship
Easyjavadeployment
gcs-buddy
Aneasy-to-usejavaclientfortheGoogleCloudStorage(GCS)API
zdutil
ToolforprovisioningGCEhadoopclusters
... and
Find more @ github.com/zulily

18. WE CONTRIBUTE BACK TO OUR COMMUNITY!
We have made contributions to open source projects:
Kubernetes
ContainerClusterManager
https://github.com/googlecloudplatform/kubernetes
Go Package for NSQ
Arealtimedistributedmessagingplatform
https://github.com/bitly/go-nsq
Google Cloud for Go
GoogleCloudAPIsGoClientLibrary
https://github.com/GoogleCloudPlatform/gcloud-golang

19. QUESTIONS?

20. CONTACT
twitter: @SaraAdineh
github.com/zulily
zulily.com/careers
engineering.zulily.com/
twitter: @zulilytech
sadineh@zulily.com