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arXiv · 2305.10530

Personalized action suggestions in low-code automation platforms

Saksham Gupta·Gust Verbruggen·Mukul Singh·Sumit Gulwani·Vu Le
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Abstract

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.

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Saksham Gupta, Gust Verbruggen, Mukul Singh, Sumit Gulwani, Vu Le. 2023-05-17. Personalized action suggestions in low-code automation platforms. https://arxiv.org/abs/2305.10530

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