How Xiaohongshu Scales Real‑Time Personalized Recommendations with Flink
The article summarizes Guo Yi’s 2019 Alibaba Cloud conference talk, outlining Xiaohongshu’s personalized recommendation architecture, detailing the data stack from ingestion to warehouse, and showcasing a Flink‑based real‑time multi‑dimensional user behavior aggregation use case, followed by a vision for the next year’s data architecture evolution.
Guo Yi, the head of recommendation architecture at Xiaohongshu, presented at the 2019 Alibaba Cloud Conference, introducing the key technologies behind the platform’s product and community personalized recommendation.
The data stack is divided into four layers: the ingestion layer, the business layer, the data service layer, and the data warehouse layer, each playing a specific role in handling massive user data.
A practical case demonstrates how the Flink stream‑processing engine provides real‑time, multi‑dimensional aggregation of user behavior for online recommendation, enabling low‑latency personalization.
The talk concludes with a forward‑looking vision for Xiaohongshu’s data architecture development in the coming year.
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