This document discusses LinkedIn's efforts to personalize users' news feeds by ranking the large number of heterogeneous activity updates. Personalization models were developed to predict a user's click-through rate based on affinities between the user and activity types as well as between the user and actors. Testing showed that personalization achieved higher click-through rates than non-personalized approaches. The models were deployed at large scale to rank billions of possible user-activity pairs daily based on past interactions. Further work aims to personalize at finer levels such as by activity topic or individual user.

1. Personalizing LinkedIn Feed
Presenter: Qi He (qhe@linkedin.com)
Other authors:
Deepak Agarwal, Bee-Chung Chen, Zhenhao Hua, Guy Levanon, Yiming Ma, Pannagadatta
Shivaswamy, Hsiao-Ping Tseng, Jaewon Yang, Liang Zhang
In SIGKDD
Aug 2015, Sydney
LinkedIn Confidential ©2015 All Rights Reserved 1

2. LinkedIn Feed
Professional network
Heterogeneous updates
 More than 40 types
 Share articles, like activities, connection updates etc.
Challenges
 Large scale (300+M members)
 Personalized relevance
 Freshness, diversity, user fatigue
How do we rank activities in a personalized way?
LinkedIn Confidential ©2015 All Rights Reserved

3. Personalization Overview
 What to show to our members?
Personalization and Ranking based on
CTR, e.g., maximize the number of
clicks per page view, which is user
specific.
 Methodologies to predict CTR
No personalization on activities
– time
– global popularity of updates
(user, context)-specific affinity
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4. No Personalization
 Reverse chronological ranking
– Fresh but not relevant
 Ranking by social popularity
– Likes, a useful signal
– CTR not monotonically related
– Not all activities have likes
 Ranking by update type
popularity
– Update type taxonomy (actor
type, verb type, object type)
– Connection : (member, connect,
member)
– Opinion: (member, like, article)
 CTR of #likes=0 is normalized as CTR=1.0;
 CTR=1.6 means +60% CTR increase.
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 The average CTR of all types is normalized
as CTR=1.0

5. Personalization: (user, context)-specific affinities
 Viewer – ActivityType Affinity: personal
preference on activity types
 Viewer-Actor Affinity: personal
preference on the actor of activity
LinkedIn Confidential ©2015 All Rights Reserved 5
impression
click

6. Viewer – ActivityType Affinity Model
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aik = the likelihood score of viewer i clicks on the activity type k.
1) Direct estimate
ˆaik =
clickik
impik
=
Cik
Iik
, for large sample sizes.
2) Feature-based model
ˆaik = f (xik;q)
3) Gamma-Poisson model
ˆaik = f (xik;q)×gik
Cik ~ Poisson(Eik × gik ), Eik = expected clicks
gik ~ Gamma(mean =1,var =
1
g
),the correction factor
ˆgik =
g +Cik
g + Eik
=
g +Cik
g + f (xe;q)
eÎIik
å
CTR correction factor + feature-based CTR
ì
í
ï
ï
ïï
î
ï
ï
ï
ï

7. Viewer – Actor Affinity Model
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viewer i, actor j, activity type k, activity t
P(Y =1) ~ Bernoulli(s(bXijkt ))
bXijkt = bij Xij + bijk Xijk + bt Xt
Xijk exists, ˆaijk = bij Xij + bijk Xijk, Viewer - Actor - ActivityType affinity
ˆaij = bij Xij, Viewer - Actor affinity
ì
í
ï
îï
Xij, Xijk : interaction features (warm-start)
Xij : member profile features (cold-start)

8. Viewer – Actor Affinity Features
 Warm-start features
– Number of past interactions (clicks,
shares, likes, …)
– Number of past impressions
– Over multiple time windows.
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impression
click

9. Viewer – Actor Affinity Features
 Cold-start features
– Viewer profile X actor profile
 Education
 Jobs
 Location
 Skills
 ……
– Social network of (viewer, actor)
 Number of common friends
 Number of viewer’s neighbors
that took actions on the same
actor
 ……
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Top N profile
features
Number of
Connections
acted on the
same actor

10. Jointly Train Click Prediction Model
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BIG DATA
Partition 1 Partition 2 Partition 3 Partition K
Logistic
Regression
Logistic
Regression
Logistic
Regression
Logistic
Regression
Consensus
Computation
ADMM - Alternating Direction Method of Multipliers

11. Affinity Deployment Framework
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 Offline
– Daily update
 Hourly: +0.1%
 2-day: -0.4%
– Viewer-ActivityType
 300M x 50: type affinity
– Viewer-Actor
 Pairs with actions in the
past half a year
 Tens of billions for
desktop and mobile
 Top 10K scores for heavy
viewers (only 0.08%
offline metric loss)
Online workflow

12. Desktop A/B Tests
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Viewer-ActivityType affinity
vs.
no affinity
Viewer-Actor affinity
vs.
Viewer-ActivityType affinity
Viewer-Actor-ActivityType affinity
vs.
Viewer-Actor affinity +
Viewer-ActivityType affinity

13. Mobile A/B Tests
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Viewer-ActivityType affinity
vs.
no affinity
Viewer-Actor-ActivityType affinity
vs.
Viewer-ActivityType affinity

14. Summary
 Conclusions
– Personalization of finer
granularity achieves higher
CTR.
– Scalability and data sparsity
are two major concerns of
production design.
 Future Work
– Activity-dependent
personalization, e.g., the
affinity between viewer and
the content topic of activity.
– Personalization at viewer id
level, e.g., each viewer has
her own personalization
model.
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15. LinkedIn Confidential ©2015 All Rights Reserved
Q&A
Qi He (qhe@linkedin.com)