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Recommender SaaS in Practice
Tianjian Chen
Jianbo Zhao
Xin Sun

Baidu Inc. 2013

http://www.baidu.com
About Us
• Baidu.com Inc.
•

Leading internet company in China

•

Reach over 500 million Internet users

•

Over 8 billio...
The Recommender SaaS Project
• Provide On-Site Recommender System for Every Website
•

http://tuijian.baidu.com (Chinese V...
Recommendation Widgets

Original
Content

Popup / Panel Slider

Original
Content

Embedded Box

http://www.baidu.com
Single On-Site RS Diagram
Recommender Trigger

New-Item

Item
Indexing

Probabilistic
Prediction
Item
Recalling

Real-time...
A Direct Solution for Scalability

http://www.baidu.com
Scale Out to Thousands of Sites
Recommender Web API

Tracking API

Recommender Engine Cluster

Engine Instance

Invert-Ind...
Global User Modeling
User Tracking
Log

JOIN in
Memory

in Real-Time
Hot Web Page
Cache

Web Crawler

Based on Stream Comp...
Project Status
• Beta release launched in April, 2013
• More than 1000 websites joined the beta test

• > 100 million page...
Inside a Recommender Engine Instance
• Combination of Multiple Sub Recommender Engines
Item Type

Item Based
CF

Content
B...
Mono RS Engine CTR Comparation
Item Type

Item Based
CF

Content
Based

Item
Popularity

Movie/Video

> 6%

~ 0.5%

> 2%

...
Things Need to Be Figured Out
• Aggregation method of different recommendations engines
• Performance loss caused by the s...
Influence of User Browsing Context
CTR

CTR
3x

5x

1x
1x

Long Term Model Short Term Model
(Minutes)
(Months)

Landing on...
Conclusions
• Break big problem down to small ones
• Wrap simple stuffs up for building complex services

• No silver bull...
Q & A Time
Thanks
chentianjian@baidu.com

http://www.baidu.com
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Recommender SaaS in Practice

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Presentation in Large Scale RS workshop, ACM RecSys 2013.
Describe the design and experiment result of Baidu Tuijian SaaS.

Published in: Technology, Business
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Transcript of "Recommender SaaS in Practice"

  1. 1. Recommender SaaS in Practice Tianjian Chen Jianbo Zhao Xin Sun Baidu Inc. 2013 http://www.baidu.com
  2. 2. 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
  3. 3. The Recommender SaaS Project • Provide On-Site Recommender System for Every Website • http://tuijian.baidu.com (Chinese Version Only For Now) Website Original Web Page Recommender System SaaS Update On-Site Content Recommendations Content Users Combination http://www.baidu.com
  4. 4. Recommendation Widgets Original Content Popup / Panel Slider Original Content Embedded Box http://www.baidu.com
  5. 5. Single On-Site RS Diagram Recommender Trigger New-Item Item Indexing Probabilistic Prediction Item Recalling Real-time User Log User Modeling Result List Control Strategy http://www.baidu.com
  6. 6. A Direct Solution for Scalability http://www.baidu.com
  7. 7. Scale Out to Thousands of Sites Recommender Web API Tracking API Recommender Engine Cluster Engine Instance Invert-Indexer Cluster Engine Instance K-V Storage Cluster Site 1 Site 5 Site 6 User C-F Site 4 Site 7 Site 9 Model Result Stream Computing Cluster Web Crawler User Tracking System http://www.baidu.com
  8. 8. Global User Modeling User Tracking Log JOIN in Memory in Real-Time Hot Web Page Cache Web Crawler Based on Stream Computing 10 Gbps Bandwidth User Browsing Session 50 Million Web Pages Cached Billions of Cookies User Preference Modeling http://www.baidu.com
  9. 9. 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
  10. 10. Inside a Recommender Engine Instance • Combination of Multiple Sub Recommender Engines Item Type Item Based CF Content Based Movie/Video X News Web X X Pic Gallery X X Novel Library X Item Popularity X Yellow Page X X [X] means particular engine has certain performance gain in recommendation of some item type http://www.baidu.com
  11. 11. Mono RS Engine CTR Comparation Item Type Item Based CF Content Based Item Popularity 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
  12. 12. 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
  13. 13. Influence of User Browsing Context CTR CTR 3x 5x 1x 1x Long Term Model Short Term Model (Minutes) (Months) Landing on Leaf Page Landing on Portal Page http://www.baidu.com
  14. 14. Conclusions • Break big problem down to small ones • Wrap simple stuffs up for building complex services • No silver bullet for an open RecSys cloud • Beside of accuracy and relevance, time efficiency is also important http://www.baidu.com
  15. 15. Q & A Time Thanks chentianjian@baidu.com http://www.baidu.com
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