Design and Implementation of a Real‑Time, Highly Available General Recommendation Platform at YHD

YHD's precision recommendation department continuously explored ways to build a real‑time, highly available, and traceable recommendation platform that could be used across the company. This article starts with the background that led to the creation of the general recommendation platform and then explains its overall architecture, the design of the recommendation process visualization system, and the recommendation result traceability system.

Personalized recommendation systems are a hot topic in e‑commerce, providing users with product suggestions that help them decide what to buy. A good system should respond in real time, offer predictability and explainability, and support both PC and mobile interfaces such as "Guess You Like" and "Customers Also Bought".

As the number of recommendation slots grew, business logic became increasingly complex, leading to duplicated code and urgent change requests. To address this, the team built a visual configuration platform that allows online, real‑time adjustment of recommendation slots and logs the recommendation logic for traceability.

The platform consists of two main subsystems: the Recommendation Process Visualization System and the Recommendation Result Traceability System. The visualization system lets operators configure slots in a UI; when a user accesses a configured slot, the system calls a unified recommendation interface, records each recommendation step via a data‑backflow framework (Kafka → Hive → HBase), and makes the logs available for the traceability subsystem.

The traceability system can replay the recommendation process by querying HBase with the request ID and item ID, thereby providing transparent, explainable recommendations and enabling rapid resolution of inaccurate recommendation complaints.

Overall, the platform’s architecture (shown in the original figures) integrates these two subsystems, reducing duplicated development effort, allowing real‑time slot adjustments, and ensuring recommendation correctness. After deployment, more than 80% of YHD’s 50+ recommendation slots on PC and mobile use the platform, development time for new recommendation needs dropped from a week to under ten minutes, and user satisfaction with recommendation results increased by nearly 50%.

In summary, the general recommendation platform has dramatically improved development efficiency and operational effectiveness for YHD’s recommendation team, providing a scalable, explainable, and high‑performance solution for personalized e‑commerce recommendations.