From Activity to Intent: Generating User Journeys with LLMs

Lin Zhu | Sr. Staff Machine Learning Engineer; Manan Kalra | Machine Learning Engineer II; Byung Eun Jeon | Sr. Machine Learning Engineer; Ye Liu | Staff Machine Learning Engineer; Xiangyi Chen | Sr. Machine Learning Engineer; Jaewon Yang | Principal Machine Learning Engineer; Jinwen Xu | Manager II, Machine Learning Engineering; Tingting Zhu | Sr. Manager, Engineering; Sudarshan Lamkhede | Director, Machine Learning Engineering

Pinterest is built to get inspired and then turn the inspiration into realization — a dinner, a renovation, a wedding, a new skill. That only works if we understand more than the Pin a user just clicked. The useful unit of personalization isn’t the thing someone just clicked, it’s what they’re trying to accomplish. Such goals unfold over weeks and months, across dozens of sessions, through hundreds of saves.

We call those goals journeys: the intersection of a user’s interests, intent, and context at a point in time. A journey is a sequence of interactions, often spanning sessions, centered on one interest and a clear intent. For example, “Summer dresses” are an interest. “Figuring out what’s in style for a summer wedding I’m attending in August” is a journey. Someone might be interested in five at once: a kitchen remodel, a vegetable garden, learning sourdough and the mix changes as life does.

If we can name those journeys, recommendations and notifications can help someone finish a project instead of bouncing between unrelated interests. This post is about how we infer them in production: a fine-tuned 4-billion-parameter large language model (LLM) that reads a chronological activity log and emits a ranked list of journey names, in the user’s locale.