User intent is inherently complex and is best modelled or represented in a nuanced and contextual manner. As I have stated in previous discussions, we cannot simply categorise user intent into informational, navigational, commercial, and transactional and expect to capture the multilayered and dynamic nature of consumer behaviour.

In the Folkscope project by the Amazon Science team, intentions were generated from co-buy transactional data across categories such as electronics and clothing. The Intention Knowledge Graph project by the same team acknowledges the challenge of understanding intentions in online platforms. The researchers also noted that many existing works on intention knowledge graphs often lack sufficient focus on connecting intentions, an aspect believed to be vital for modelling user behaviour and predicting future user actions.
Abstract intentions play a useful role as the conceptual umbrella under which concrete intentions are connected.