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LinkedIn Confidential ©2015 All Rights Reserved
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 SIGKDDAug 2015, Sydney
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
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
LinkedIn Confidential ©2015 All Rights Reserved 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.
The average CTR of all types is normalized as CTR=1.0
LinkedIn Confidential ©2015 All Rights Reserved 5
Personalization: (user, context)-specific affinities
Viewer – ActivityType Affinity: personal preference on activity types
Viewer-Actor Affinity: personal preference on the actor of activity
impression
click
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Viewer – ActivityType Affinity Model
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Viewer – Actor Affinity Model
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Viewer – Actor Affinity Features
Warm-start features– Number of past interactions (clicks,
shares, likes, …)– Number of past impressions– Over multiple time windows.
impression
click
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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
……
Top N profilefeatures
Number ofConnectionsacted on thesame actor
Jointly Train Click Prediction Model
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BIG DATA
Partition 1 Partition 2 Partition 3 Partition K
LogisticRegression
LogisticRegression
LogisticRegression
LogisticRegression
ConsensusComputation
ADMM - Alternating Direction Method of Multipliers
Affinity Deployment Framework
LinkedIn Confidential ©2015 All Rights Reserved 11
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
LinkedIn Confidential ©2015 All Rights Reserved 12
Desktop A/B Tests
Viewer-ActivityType affinity vs. no affinity
Viewer-Actor affinity vs. Viewer-ActivityType affinity
Viewer-Actor-ActivityType affinity vs. Viewer-Actor affinity + Viewer-ActivityType affinity
LinkedIn Confidential ©2015 All Rights Reserved 13
Mobile A/B Tests
Viewer-ActivityType affinity vs. no affinity
Viewer-Actor-ActivityType affinity vs. Viewer-ActivityType affinity
LinkedIn Confidential ©2015 All Rights Reserved 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.
LinkedIn Confidential ©2015 All Rights Reserved
Q&A
Qi He (qhe@linkedin.com)
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