用RippleNet增强新闻冷启动推荐，支持大规模媒体平台实时部署。
Solving cold start in news recommendations: a RippleNet-based system for large scale media outlet
- 将内容嵌入融合进RippleNet的知识传播机制，解决新内容冷启动问题。
- 在Onet.pl平台实现分布式训练与推理，支持大规模用户行为数据处理。
- 构建可扩展的黄金数据集，便于接入新特征信号，适合工业级推荐系统参考。
我们提出一种基于RippleNet的可扩展推荐系统，针对波兰最大在线媒体平台Onet.pl的媒体领域定制化部署。该方案通过将内容嵌入整合至RippleNet的知识传播机制，有效解决新发布内容的冷启动问题，实现对未见项目的精准打分。系统采用Amazon SageMaker进行分布式训练与推理，利用Apache Airflow编排数据管道与模型重训练流程。为保障训练数据质量，构建了包含用户与物品特征及独立交互表的综合性黄金数据集，支持灵活扩展与新信号集成。
原文摘要 · Abstract (English)
We present a scalable recommender system implementation based on RippleNet, tailored for the media domain with a production deployment in Onet.pl, one of Poland's largest online media platforms. Our solution addresses the cold-start problem for newly published content by integrating content-based item embeddings into the knowledge propagation mechanism of RippleNet, enabling effective scoring of previously unseen items. The system architecture leverages Amazon SageMaker for distributed training and inference, and Apache Airflow for orchestrating data pipelines and model retraining workflows. To ensure high-quality training data, we constructed a comprehensive golden dataset consisting of user and item features and a separate interaction table, all enabling flexible extensions and integration of new signals.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。