立场论文：为何“盲射”方法主导推荐系统实践；呼吁摒弃反乌托邦思维
Position Paper: Why the Shooting in the Dark Method Dominates Recommender Systems Practice; A Call to Abandon Anti-Utopian Thinking
AI总结：
本文质疑推荐系统实践中以代理指标代替性能优化的反乌托邦思维，主张通过非标准使用深度学习栈来直接优化奖励，从而改进推荐效果。
摘要：
应用推荐系统研究处于一个奇特的位置。虽然通过A/B测试来衡量性能有着非常严格的协议，但寻找要测试的“B”的最佳实践并不明确地针对性能，而是针对一个代理指标。给定A/B测试的成功或失败完全取决于所提出的代理指标是否比之前的代理指标与性能的相关性更好。目前不存在任何原则可以在离线状态下判断一个代理指标是否优于另一个，这使得从业者只能盲射。本立场论文的目的是质疑这种反乌托邦思维，并主张对深度学习栈进行非标准使用实际上有潜力解锁奖励优化的推荐。
英文摘要：
Applied recommender systems research is in a curious position. While there is a very rigorous protocol for measuring performance by A/B testing, best practice for finding a `B' to test does not explicitly target performance but rather targets a proxy measure. The success or failure of a given A/B test then depends entirely on if the proposed proxy is better correlated to performance than the previous proxy. No principle exists to identify if one proxy is better than another offline, leaving the practitioners shooting in the dark. The purpose of this position paper is to question this anti-Utopian thinking and argue that a non-standard use of the deep learning stacks actually has the potential to unlock reward optimizing recommendation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
