发表机构
California State University Chico（加州州立大学奇科分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对股票推荐系统面临的挑战，提出基于分布式微服务架构的可扩展在线深度学习方法，采用混合主从架构，利用相关技术实现高可用性，能快速提供推荐且处理大量请求，贡献了金融领域在线推荐系统参考架构。
AI 中文摘要
股票推荐系统面临着既要适应快速变化的市场状况，又要为终端用户保持低延迟预测的双重挑战。传统的批量训练模型无法捕捉概念漂移，整体架构在负载下难以提供容错能力。本文提出了一种基于分布式微服务架构的可扩展在线深度学习股票推荐系统，该架构使用了Kubernetes、Docker和RabbitMQ。系统采用混合主从架构，主模型持续对来自Alpha Vantage API的包括每股收益、MACD和价格等流式金融数据进行训练，多个副本模型并行提供面向用户的推荐。通过TensorFlow Recommenders实现的多层感知器利用显式用户评分（1 - 5）和迁移学习生成基于内容的推荐。该架构确保了高可用性，主模型将权重持久化到谷歌云对象存储，副本在失败时能无缝恢复，RabbitMQ提供消息持久性和重放功能。结果表明，该系统每个请求23秒就能提供股票推荐，每个副本每秒能处理多达500个投资组合添加请求。主要局限包括数据陈旧（由于API速率限制可达150分钟）以及缺乏用于集群间安全的服务网格。这项工作为在线推荐系统贡献了一个可投入生产的参考架构，在金融领域背景下平衡了一致性、可用性和可扩展性。
英文摘要
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
Comments6 pages, 4 figures
