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.
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Recommender SaaS in Practice
- 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