A scalable online machine learning approach for Stock Recommendation

• URL: http://arxiv.org/abs/2607.23120v1
• Date: Sat, 25 Jul 2026 09:32:55 GMT
• Title: A scalable online machine learning approach for Stock Recommendation
• Abstract summary: This paper presents a scalable online deep learning-based stock recommendation system built on a distributed architecture using Docker, RabbitMQ and Google Cloud Object Storage.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

Related papers

• INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration [18.376319491516266]
  We introduce INFRAMIND, a framework that makes the entire multi-agent stack infrastructure-aware.<n>An infra-aware executor then observes per-model queue depths, cache utilization, and response latencies at each agent step to decide which model to call.<n>A budget-aware scheduler further reorders each model's queue so that urgent requests are served first.
  arXiv  Detail & Related papers  (2026-06-09T20:50:12Z)
• Stacked from One: Multi-Scale Self-Injection for Context Window Extension [69.24689919827817]
  modelname is a novel framework based on multi-grained context compression and query-aware information acquisition.<n>modelnameachieves performance superior or comparable to strong baselines.
  arXiv  Detail & Related papers  (2026-03-05T03:16:16Z)
• SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models [53.19726629537694]
  Post-training alignment of video generation models with human preferences is a critical goal.<n>Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer from labeling noise.<n>We propose SoliReward, a systematic framework for video RM training.
  arXiv  Detail & Related papers  (2025-12-17T14:28:23Z)
• Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders [11.073761978382398]
  We propose a novel two-stage modeling framework, namely VIrtual Sequential Target Attention (VISTA)<n>VISTA decomposes traditional target attention from a candidate item to user history items into two distinct stages.<n>Our approach achieves significant improvements in offline and online metrics and has been successfully deployed on an industry leading recommendation platform.
  arXiv  Detail & Related papers  (2025-10-24T22:17:49Z)
• Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs [0.8246494848934447]
  This paper introduces a meta-learning framework, that can be used to perform parameter-efficient prompt-tuning.<n>On MovieLens-1M, Amazon Reviews, and Recbole, we can see that our adaptive model outperforms strong baselines in NDCG@10, HR@10, and MRR.<n>Its 275 ms rate of adaptation allows successful real-time risk profiling of financial systems.
  arXiv  Detail & Related papers  (2025-07-22T15:07:23Z)
• Adaptable Embeddings Network (AEN) [49.1574468325115]
  We introduce Adaptable Embeddings Networks (AEN), a novel dual-encoder architecture using Kernel Density Estimation (KDE) AEN allows for runtime adaptation of classification criteria without retraining and is non-autoregressive. The architecture's ability to preprocess and cache condition embeddings makes it ideal for edge computing applications and real-time monitoring systems.
  arXiv  Detail & Related papers  (2024-11-21T02:15:52Z)
• ConServe: Fine-Grained GPU Harvesting for LLM Online and Offline Co-Serving [61.35068981176018]
  ConServe is a large language model (LLM) serving system that achieves high throughput and strong online latency guarantees.<n>We show that ConServe delivers an average of 2.2$times$ higher throughput and reduces online serving tail latency by 2.9$times$ on average compared to state-of-the-art systems.
  arXiv  Detail & Related papers  (2024-10-02T04:12:13Z)
• A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models [6.823233135936128]
  Recommendation systems are crucial for a variety of modern apps and web services, such as news feeds, social networks, e-commerce, search, etc. To achieve peak prediction accuracy, modern recommendation models combine deep learning with terabyte-scale embedding tables to obtain a fine-grained representation of the underlying data. Traditional inference serving architectures require deploying the whole model to standalone servers, which is infeasible at such massive scale.
  arXiv  Detail & Related papers  (2022-10-17T07:36:18Z)
• BagPipe: Accelerating Deep Recommendation Model Training [9.911467752221863]
  Bagpipe is a system for training deep recommendation models that uses caching and prefetching to overlap remote embedding accesses with the computation. We design an Oracle Cacher, a new component that uses a lookahead algorithm to generate optimal cache update decisions.
  arXiv  Detail & Related papers  (2022-02-24T23:54:12Z)
• Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System [54.588242387136376]
  We introduce KaiS, a learning-based scheduling framework for edge-cloud systems. First, we design a coordinated multi-agent actor-critic algorithm to cater to decentralized request dispatch. Second, for diverse system scales and structures, we use graph neural networks to embed system state information. Third, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration.
  arXiv  Detail & Related papers  (2021-01-17T03:45:25Z)
• A Generic Network Compression Framework for Sequential Recommender Systems [71.81962915192022]
  Sequential recommender systems (SRS) have become the key technology in capturing user's dynamic interests and generating high-quality recommendations. We propose a compressed sequential recommendation framework, termed as CpRec, where two generic model shrinking techniques are employed. By the extensive ablation studies, we demonstrate that the proposed CpRec can achieve up to 4$sim$8 times compression rates in real-world SRS datasets.
  arXiv  Detail & Related papers  (2020-04-21T08:40:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.