RePlay让推荐系统研发与生产无缝衔接，支持灵活扩展。
RePlay: a Recommendation Framework for Experimentation and Production Use
- 统一接口实现从研究到生产全流程
- 支持Pandas/Polars/Spark多计算栈自由切换
- 适合需快速落地推荐模型的数据团队
使用单一工具构建并比较推荐系统，可显著缩短新模型的上市时间，且对比结果更一致。因此，近期出现了多种面向推荐领域的工具和库。然而，多数框架仅面向研究人员，难以直接用于生产，主要受限于无法处理大规模数据或架构设计不合理。在本次演示中，我们介绍开源工具RePlay——一个包含端到端流程的推荐系统框架，专为生产环境设计。RePlay支持在不同阶段选用Pandas、Polars或Spark作为计算引擎，实现计算可扩展并部署至集群。这使得数据科学家能通过统一接口，轻松实现从研究模式到生产模式的过渡。
原文摘要 · Abstract (English)
Using a single tool to build and compare recommender systems significantly reduces the time to market for new models. In addition, the comparison results when using such tools look more consistent. This is why many different tools and libraries for researchers in the field of recommendations have recently appeared. Unfortunately, most of these frameworks are aimed primarily at researchers and require modification for use in production due to the inability to work on large datasets or an inappropriate architecture. In this demo, we present our open-source toolkit RePlay - a framework containing an end-to-end pipeline for building recommender systems, which is ready for production use. RePlay also allows you to use a suitable stack for the pipeline on each stage: Pandas, Polars, or Spark. This allows the library to scale computations and deploy to a cluster. Thus, RePlay allows data scientists to easily move from research mode to production mode using the same interfaces.
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。