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

1. Recommender SaaS in Practice
Tianjian Chen
Jianbo Zhao
Xin Sun
Baidu Inc. 2013
http://www.baidu.com

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. 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. Recommendation Widgets
Original
Content
Popup / Panel Slider
Original
Content
Embedded Box
http://www.baidu.com

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. A Direct Solution for Scalability
http://www.baidu.com

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. 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. 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. 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. 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. 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. 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. 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. Q & A Time
Thanks
chentianjian@baidu.com
http://www.baidu.com