Automation platforms aim to automate repetitive tasks using workflows, which start with a trigger and then perform a series of actions. However, with many possible actions, the user has to search for the desired action at each step, which hinders the speed of flow development. We propose a personalized transformer model that recommends the next item at each step. This personalization is learned end-to-end from user statistics that are available at inference time. We evaluated our model on workflows from Power Automate users and show that personalization improves top-1 accuracy by 22%. For new users, our model performs similar to a model trained without personalization.

翻译：自动化平台旨在通过工作流自动执行重复性任务，这些工作流由触发器启动并执行一系列动作。然而，由于存在大量可能的动作，用户在每个步骤中都需要搜索所需的动作，从而阻碍了流程开发的速度。我们提出一种个性化Transformer模型，可在每个步骤中推荐下一项动作。这种个性化通过推理时可获取的用户统计数据进行端到端学习。我们在Power Automate用户的工作流上评估了该模型，结果表明个性化将top-1准确率提升了22%。对于新用户，我们的模型表现与未使用个性化训练的模型相当。