AI tech meetup event at linkedin on 5/9, 2019. https://learn.xnextcon.com

1. AI to create professional
opportunities
Liang Zhang
Director of Engineering, AI
AI Tech Meetup 5/9/19

2. We are all seeking new professional opportunities
To Advance our Careers
Let people know you’re open

3. LinkedIn operates the largest professional network on the Internet
630M+ members
50K+ skills
25K+
titles
200+
countries
30M+ orgs
148 industries
certificates,
degrees, and
more ...states, cities,
postal codes, ...
roles, occupations
speciality
tools, products,
technologies, ...
Tell your
Story

4. AI is like oxygen at LinkedIn

5. People You May Know Feed

6. Jobs Learning

7. Recruiter Search Sales

8. Our Scale
25 B
ML A/B experiments
per week
data processed offline
per day
2002.15 PB
data processed
nearline per day
2 PB
graph edges with 1B
nodes
53 B
parameters in ML
models

9. Our Technology

10. Personalization at Scale
Personalization models creates significant value for LinkedIn
10
Basic model: Feature-based wide-and-deep
▪ Good for modeling general user behavior
▪ Not effective to capture the idiosyncratic
behavior of individual users and individual items
Basic model with real-time statistics
as features:
▪ Examples of real-time statistics:
– #clicks on item i
– #times user u clicked items of
topic k
▪ Good for statistics with sufficient
sample sizes
▪ Not effective when the sample
sizes of the statistics are small =>
high variance, often required to
capture segmented popularity
Linear Neural Net Trees

11. A class of flexible model that we find useful for personalization
Global Model
(Macro)
Per-User Model
(Micro)
Per-Item Model
(Micro)
Learns general user
behavior using a wide-
and-deep model with real-
time statistics.
Example: Users like to read
about articles on topics
related to their skills
Learns the behavior of an
individual user
One model per user
Example: Deepak likes to
read about articles on
startups, IoT and Cloud
Deepak’s own model has
100s of parameters to
capture his particular
behavior
Learns the behavior of an
individual item
One model per item
Example: Article on Kai-Fu
Lee’s book is liked by people
in Silicon Valley with
Marketing skills
Kai-Fu’s article has 100s of
parameters
Generalized Additive Mixed Effect Model

12. Reinforcement Learning with Immediate Rewards
Perform reinforcement learning with immediate rewards via Thompson Sampling to collect the most
useful data for the micro models
Evaluate models offline using historical replay on randomized data (with inverse-sampling-rate
weighting) to mitigate bias in data collection
Current Work
▪ Learning nonparametric priors for per-entity models via Neural embedding models
▪ Full reinforcement Learning to directly optimize for long-term rewards like DAU, confirmed hires, etc
12
Global
Model
Per-User
Model
Per-Item
Model
Estimate the posterior distribution of model parameters and draw a score
from the distribution

13. Search @ LinkedIn
▪ Traditional Search Engine (Google, Bing…): MaxDocSim <Query, Doc>
▪ LinkedIn Search Engines (Personalized): MaxDocSim <Query, Doc> | Person (social network, profile,
interactions…)
▪ Approach: Traditional IR + NLP via Deep Neural Network + Personalization
Search Personalization

14. Video @ LinkedIn
Feed Search Learning
Content Quality
Computer Vision + Speech Recognition + NLP

15. 15
Pro-ML: Productive Machine Learning
▪ Make the end-to-end process of running and iterating on large ML workflows easy, robust and almost automated
Model
Deployment
Model
Maintenance
Feature
Engineering
Target
Definition
Model
Creation
#Experiments per Eng
Business
Impact

16. Core Team
16
SNVSunnyvale
SFSan Francisco
DUB Dublin
BLR Bangalore
NYC New York City
500+
Eng.

17. We are hiring!