How Online AI Transforms Search and Recommendation Systems
At Alibaba's 2016 Double 11 Tech Forum, researcher Xu Yinghui presented how online AI technologies enhance search and recommendation on the e‑commerce platform, turning massive user behavior data into actionable insights that improve traffic allocation and maximize welfare for consumers, sellers, and the platform.
Online AI Technology in Search and Recommendation Scenarios
Speaker: Xu Yinghui, Alibaba Researcher
Bio: Ph.D. in Computer Science from Toyohashi University of Technology, leads search ranking and foundational algorithms at Alibaba's search division. Designed the next‑generation ranking framework, built an offline‑nearline‑online personalized search system, and scaled real‑time online computation.
Talk abstract: Search and recommendation act as intelligent hubs on Alibaba’s e‑commerce platform, turning massive user behavior data into structured sequences, predicting outcomes, and guiding traffic allocation to maximize welfare for consumers, sellers, and the platform. The system is evolving from pure machine‑learning‑driven solutions toward integrated learning‑and‑decision capabilities that can explore objectives under uncertainty.
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