提出时间窗口划分的推荐评估框架，解决数据泄露问题。
RecNextEval: A Reference Implementation for Temporal Next-Batch Recommendation Evaluation
- 采用时间窗口划分数据，避免未来信息泄漏。
- 支持真实生产环境下的批量推荐评估。
- 开源工具包+图形界面，便于复现与对比实验。
推荐系统研究中已有大量工具包以促进公平评估和可复现性。然而，近期对推荐评估流程的批判性审查引发了对现有评估流程有效性的担忧。本文展示RecNextEval——一个专为下一批次推荐设计的评估框架的参考实现。该框架采用时间窗口数据划分方式，确保模型沿全局时间线进行评估，有效减少数据泄露。其实施凸显了推荐系统评估中的固有复杂性，并倡导向更贴近生产环境的模型开发范式转变。RecNextEval库及其配套图形界面已开源并公开可用。
原文摘要 · Abstract (English)
A good number of toolkits have been developed in Recommender Systems (RecSys) research to promote fair evaluation and reproducibility. However, recent critical examinations of RecSys evaluation protocols have raised concerns regarding the validity of existing evaluation pipelines. In this demonstration, we present RecNextEval, a reference implementation of an evaluation framework specifically designed for next-batch recommendation. RecNextEval utilizes a time-window data split to ensure models are evaluated along a global timeline, effectively minimizing data leakage. Our implementation highlights the inherent complexities of RecSys evaluation and encourages a shift toward model development that more accurately simulates production environments. The RecNextEval library and its accompanying GUI interface are open-source and publicly accessible.
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。