用离线评价预测工作推荐系统的在线性能

Adrien Mogenet, T. Pham, Masahiro Kazama, Jiali Kong
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引用次数: 5

摘要

事实上,推荐系统是用来推荐工作的。在这种情况下,我们可以收集的隐式和显式反馈信号是罕见的事件,这使得评估任务更加复杂。在线评估(A/B测试)通常是衡量实验结果的最可靠方法,但这是一个缓慢的过程。相比之下,离线评估过程更快,但使其可靠是至关重要的,因为它告知我们在生产中推出新改进的决定。在本文中,我们回顾了三种推荐模型的比较离线和在线性能,我们描述了我们使用的评估指标,并分析了离线性能指标与在线指标之间的关系,以了解如何利用离线评估过程来为决策提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting online performance of job recommender systems with offline evaluation
At Indeed, recommender systems are used to recommend jobs. In this context, implicit and explicit feedback signals we can collect are rare events, making the task of evaluation more complex. Online evaluation (A/B testing) is usually the most reliable way to measure the results from our experiments, but it is a slow process. In contrast, the offline evaluation process is faster, but it is critical to make it reliable as it informs our decision to roll out new improvements in production. In this paper, we review the comparative offline and online performances of three recommendations models, we describe the evaluation metrics we use and analyze how the offline performance metrics correlate with online metrics to understand how an offline evaluation process can be leveraged to inform the decisions.
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