Personalizing LinkedIn Feed

LinkedIn Confidential ©2015 All Rights Reserved

Personalizing LinkedIn Feed

Presenter: Qi He ([email protected])

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 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


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


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


Viewer – ActivityType Affinity Model


Viewer – Actor Affinity Model


Viewer – Actor Affinity Features

Warm-start features– Number of past interactions (clicks,

shares, likes, …)– Number of past impressions– Over multiple time windows.

impression

click


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


BIG DATA

Partition 1 Partition 2 Partition 3 Partition K

LogisticRegression

LogisticRegression

LogisticRegression

LogisticRegression

ConsensusComputation

ADMM - Alternating Direction Method of Multipliers

Affinity Deployment Framework


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


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


Mobile A/B Tests

Viewer-ActivityType affinity vs. no affinity

Viewer-Actor-ActivityType affinity vs. Viewer-ActivityType affinity


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.


Q&A

Qi He ([email protected])

Personalizing LinkedIn Feed

Documents

Transcript of Personalizing LinkedIn Feed