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Recommender SaaS in Practice

Recommender SaaS in Practice. Tianjian Chen Baidu Inc . 2013. About Us. Baidu.com Inc. Leading internet company in China Reach over 500 million Internet users O ver 8 billion PV/day of web search, online advertising and social network services. The Recommender SaaS Project.

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Recommender SaaS in Practice

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  1. Recommender SaaS in Practice Tianjian Chen Baidu Inc. 2013

  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

  3. The Recommender SaaSProject • Provide On-Site Recommender System for Every Website • http://tuijian.baidu.com (Chinese Version Only For Now) Recommender System SaaS Update Website Original Web Page On-Site Content Recommendations Content Combination Users

  4. Recommendation Widgets Original Content Original Content Popup / Panel Slider Embedded Box

  5. 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.

  6. Single On-Site RS Diagram Recommender Trigger New-Item Item Indexing Probabilistic Prediction Result List Item Recalling Control Strategy Real-time User Log User Modeling

  7. A Direct Solution for Scalability

  8. Scale Out to Thousands of Sites Recommender Web API Tracking API Recommender Engine Cluster Engine Instance Engine Instance Invert-Indexer Cluster K-V Storage Cluster Site 1 Site 4 Site 5 Site 7 Site 6 Site 9 User Model C-F Result Stream Computing Cluster Web Crawler User Tracking System

  9. in Real-Time！ Global User Modeling Web Crawler User Tracking Log Hot Web Page Cache Based on Stream computing 10 Gbps Bandwidth 50 Million Web Pages Billions ofCookies JOIN in Memory User Browsing Session User Preference Modeling

  10. Inside a Recommender Engine Instance • Combination of Multiple Sub Recommender Engines [X] means particular engine has certain performance gain in recommendation of some item type

  11. Mono RS Engine CTR Comparation • 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.

  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…

  13. Influence of User Browsing Context CTR CTR 3x 5x 1x 1x Short Term Model (Minutes) Landing on Leaf Page Landing on Portal Page Long Term Model (Months)

  14. Q & A Time • Thanks

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