Browsemap: Collaborative Filtering at LinkedIn

Recruiting Solutions Recruiting Solutions Recruiting Solutions 1

Browsemap: Collaborative Filtering At LinkedIn

Lili Wu, Sam Shah, Sean Choi, Mitul Tiwari, Christian Posse

RSWeb 2014 with RecSys

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Agenda § Motivation § Architecture § Applications §  Lessons Learned

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Profile Browsemap: People who viewed this profile also viewed… Count co-views

Collaborative filtering for member profile

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Collaborative filtering for job page

Job Browsemap: People who viewed this job also viewed… Count co-views

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company group portfolio

… many CF based recommenders

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•  Many different entities

•  Similar problems with different requirement •  Fast product development cycle

•  Hybrid recommender systems

•  Handle LinkedIn data volume and traffic

Challenges

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Challenges

è Horizontal Platform

•  Many different entities

•  Similar problems with different requirement •  Fast product development cycle

•  Hybrid recommender systems

•  Handle LinkedIn data volume and traffic

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Browsemap

Collaborative Filtering Platform at LinkedIn

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Browsemap Platform

•  Scalability Ø  Online/offline architecture Ø  Hundreds of millions of entities, billions of

monthly page views •  Browsemap Domain Specific Language (DSL)

Ø  Code reuse through modular components Ø  Flexible computation workflow construction

•  Data are used by hybrid recommenders

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Browsemap Architecture

HDFS

User Activity

Data

Frontend Services

Results Queries

Hadoop

Browsemap Engine

Browsemap DSL Online

Query API

Key-value storage

Voldemort

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Browsemap Architecture

HDFS

Frontend Services

Results Queries

Hadoop

Browsemap Engine

Browsemap DSL Online

Query API

Key-value storage

Voldemort

User Activity

Data High Throughput

12 12 12

Browsemap Architecture

HDFS

Frontend Services

Results Queries

Hadoop

Browsemap Engine

Browsemap DSL Online

Query API

Key-value storage

Voldemort

User Activity

Data Low Latency

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Browsemap Domain Specific Language (DSL)

Module Collection

Co-view counting

Spam User Filtering

Expired Job Filtering

Expired Job Filtering

Cold-start techniques

Co-view counting

…

Cold-start techniques

… Job browsemap

���

Job ��� Company

…

Spam User Filtering

Co-view counting

…

Cold-start techniques

…

Spam User Filtering

Company browsemap

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•  Support all entity types •  Adjust to each product requirement

•  Scale

Recap

Voldemort

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Agenda ü Motivation ü Architecture § Applications §  Lessons Learned

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Applications – CF based recommenders Profile Browsemap

Portfolio Browsemap

Job Browsemap Group Browsemap

Hiring Browsemap

Company Browsemap

Influencer Browsemap

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Applications – Hybrid Recommender Systems

Suggested Profile Update

Swee Lim

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Applications – Hybrid Recommender Systems

Suggested Profile Update

Goal: for each member,

find companies he may want to follow

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Applications – Hybrid Recommender Systems

Google Cisco Member followed companies

Linkedin, Facebook

Juniper, Arista Companies user may

be interested in

…

…

Member info: •  Content-based features

title, industry, location, … •  Collaborative filtering feature

Co-follow Browsemaps: People who follow this company also follow these companies

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Applications – Hybrid Recommender Systems

Question: For a company C, will member M like it?

Approach: Logistic regression Features:

member location company location 1 if yes, 0 if no

company is in the list of the co-follow browsemaps ? 1 if yes, 0 if no

…

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Applications – Hybrid Recommender Systems

Collaborative Filtering is important: •  Surface implicit connection between companies •  Based on Member’s preference

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Agenda ü Motivation ü Architecture ü Applications §  Lessons Learned

Lesson 1: Tall oaks grow from little acorns

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Lesson 1: Tall oaks grow from little acorns

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Lesson 1: Tall oaks grow from little acorns

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Lesson 1: Tall oaks grow from little acorns

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A generic horizontal platform is essential

Lesson 2: One hand washes the other

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Job Browsemap

Similar Jobs

Collaborative filtering: “Follower audience”

Content based: “Leader audience”

Lesson 3: You can’t get blood out of a stone

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Job 1 Job 2 Job 3 (new)

Need to handle cold start problem

(view time)

merge

Leverage Browsing History Personalized Backfill

Lesson 4: A chain is only as strong as its weakest link

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CF: Relies solely on user activities Good data is crucial

§  Mistakes can be hard to detect / debug

§  Simple mistakes can have big impact e.g. “jobid” à “id”

§  Need prevention mechanism Ø  Improve tracking Ø  Unit test Ø  Browsemap platform data-check :

input volume, coverage/metrics analysis

Lesson 5: User experience matters

50% CTR

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500% more applications

ª  Put recommendations in user’s flow

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§  Collaborative filtering is important for LinkedIn

§  Browsemap is in production for 3+ years §  Horizontal platform is crucial

Conclusion

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§  Questions?

Thank you !