The timed position of documents retrieved by learning to rank models can be seen as signals. Signals carry useful information such as drop or rise of documents over time or user behaviors. In this work, we propose to use the logic formalism called Signal Temporal Logic (STL) to characterize document behaviors in ranking accordingly to the specified formulas. Our analysis shows that interesting document behaviors can be easily formalized and detected thanks to STL formulas. We validate our idea on a dataset of 100K product signals. Through the presented framework, we uncover interesting patterns, such as cold start, warm start, spikes, and inspect how they affect our learning to ranks models.

中文翻译：

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通过信号时间逻辑分析电子商务排名信号

通过学习对模型进行排名而检索到的文档的定时位置可以看作是信号。信号携带有用的信息，例如文档随时间推移或用户行为的下降或上升。在这项工作中，我们建议使用称为信号时态逻辑（STL）的逻辑形式主义来表征文档行为，从而根据指定的公式进行排名。我们的分析表明，借助STL公式，可以轻松地形式化和检测有趣的文档行为。我们在10万个产品信号的数据集上验证了我们的想法。通过提出的框架，我们发现有趣的模式，例如冷启动，热启动，峰值，并检查它们如何影响我们对模型进行排名的学习。