基于流式数据的可扩展股票推荐系统，支持低延迟实时推荐。
A scalable online machine learning approach for Stock Recommendation
- 采用分布式微服务架构，主模型持续学习流式金融数据，副本并行响应请求。
- 每请求推荐耗时23秒，单副本每秒处理500个组合添加请求，性能稳定。
- 适合需要高可用、低延迟的金融推荐场景，尤其关注系统容错与扩展性。
股票推荐系统面临快速变化的市场环境与低延迟响应的双重挑战。传统批量训练模型难以捕捉概念漂移，而单体架构在负载下缺乏容错能力。本文提出一种基于分布式微服务架构的在线深度学习股票推荐系统，采用Kubernetes、Docker与RabbitMQ构建。系统采用混合主从结构：主模型持续从Alpha Vantage API接收包括每股收益（EPS）、MACD和价格在内的流式数据进行训练，多个副本模型并行提供用户推荐服务。使用TensorFlow Recommenders实现多层感知机，结合显式用户评分（1-5分）与迁移学习生成内容推荐。主模型将权重持久化至Google Cloud Object Storage，确保副本故障后可无缝恢复；RabbitMQ保障消息持久化与重播。实验表明，系统每请求推荐耗时23秒，单个从节点每秒可处理500个组合添加请求。主要局限为数据滞后（因API限速最高达150分钟），且未引入服务网格实现跨集群安全。该工作为金融领域在线推荐系统提供了兼具一致性、可用性与可扩展性的生产级参考架构。
原文摘要 · Abstract (English)
Stock recommendation systems face the dual challenge of adapting to rapidly changing market conditions while maintaining low-latency predictions for end users. Traditional batch-trained models fail to capture concept drift, and monolithic architectures struggle to provide fault tolerance under load. This paper presents a scalable online deep learning-based stock recommendation system built on a distributed microservices architecture using Kubernetes, Docker, and RabbitMQ. The system employs a hybrid leader-follower architecture where a primary model continuously trains on streaming financial data, including EPS, MACD, and price, from the Alpha Vantage API while multiple replica models serve user-facing recommendations in parallel. A multilayer perceptron implemented with TensorFlow Recommenders generates content-based recommendations using explicit user ratings (1-5) and transfer learning. The architecture ensures high availability. The leader persists model weights to Google Cloud Object Storage, allowing replicas to recover seamlessly upon failure, while RabbitMQ provides message durability and replay. Results demonstrate that the system serves stock recommendations in 23 seconds per request and processes up to 500 portfolio addition requests per second per follower. Key limitations include data staleness (up to 150 minutes due to API rate limits) and the absence of a service mesh for inter-cluster security. This work contributes a production-ready reference architecture for online recommender systems that balances consistency, availability, and scalability in a financial domain context
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