More Related Content Similar to Personalizing LinkedIn Feed (20) Personalizing LinkedIn Feed1. 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
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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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Editor's Notes fatigue [fəˈtiɡ] Chronological [ˌkrɑ:nəˈlɑ:dʒɪkl] A balance between cold-start features and warm-start features.