http://www.baidu.com

Tianjian Chen

Baidu Inc. 2013

Recommender SaaS in Practice

http://www.baidu.com

About Us

• Baidu.com Inc.• Leading internet company in China

• Reach over 500 million Internet users

• Over 8 billion PV/day of web search, online advertising and social network services

http://www.baidu.com

The Recommender SaaS Project

• Provide On-Site Recommender System for Every Website• http://tuijian.baidu.com (Chinese Version Only For Now)

Users

Recommender SystemSaaS

WebsiteUpdate

ContentCombination

Original Web Page On-Site Content Recommendations

http://www.baidu.com

Recommendation Widgets

Popup / Panel Slider Embedded Box

Original Content

Original Content

http://www.baidu.com

Project Status

• Beta release launched in April, 2013

• More than 1000 websites joined the beta test

• > 100 million page views every day

• Avg. CTR 3%• from 2% to 20% depending on different types of websites.

http://www.baidu.com

Single On-Site RS Diagram

ItemIndexingNew-

Item

Real-time

User Log

UserModeling

ItemRecalling

ProbabilisticPrediction

Control Strategy

Result List

Recommender Trigger

http://www.baidu.com

A Direct Solution for Scalability

http://www.baidu.com

Scale Out to Thousands of Sites

Engine Instance Engine Instance

Invert-Indexer Cluster K-V Storage Cluster

Recommender Engine Cluster

Site 1Site 4

Site 5Site 7

Site 6Site 9

UserModel

C-F Result

Web Crawler User Tracking System

Stream Computing Cluster

Recommender Web API Tracking API

http://www.baidu.com

Global User Modeling in Real-Time！User Tracking

LogHot Web Page

Cache

JOIN in Memory

User BrowsingSession

Based on Stream computing 10 Gbps Bandwidth

50 Million Web Pages

Billions of Cookies

Web Crawler

User PreferenceModeling

http://www.baidu.com

Inside a Recommender Engine Instance

Item Type Item BasedCF

Semantic Similarity

ItemPopularity

Movie/Video X X

News Web X X

Pic Gallery X X

Novel Library X X

Yellow Page X

• Combination of Multiple Sub Recommender Engines

[X] means particular engine has certain performance gain in recommendation of some item type

http://www.baidu.com

Mono RS Engine CTR Comparation

Item Type Item BasedCF

Semantic Similarity

ItemPopularity

Movie/Video > 6% ~ 0.5% > 2%

News Web ~ 7% > 25% ~ 0.5%

Pic Gallery > 6% ~ 4% ~ 1%

Novel Library > 10% ~ 8% ~ 1%

Yellow Page ~ 1% ~ 1% > 15%

• IBCF is handy, but not the silver bullet

• To our surprise, IP doesn’t work for News Recommendation

• No one like old yellow page posts, even they are semantically or statistically relevant.

http://www.baidu.com

Things Need to Be Figured Out

• Aggregation method of different recommendations engines

• Performance loss caused by the site owners’ preset rules

• Item longevity detection / prediction

• URL normalization

• And…

http://www.baidu.com

Influence of User Browsing Context

Long Term Model(Months)

Short Term Model(Minutes)

1x

3x

CTR

Landing onLeaf Page

Landing onPortal Page

1x

5x

CTR

http://www.baidu.com

Q & A Time

Thanks