📝 SummarXiv

Abstract

Understanding how user preference evolves over time is a fundamental challenge central to modern digital ecosystems, for which Large Language Models (LLMs) are an increasingly prominent and popular approach due to their ability to comprehend the rich semantic context within behavioral data. A common practice is to use LLMs to predict a user's next action by directly generating a ranked list of preferred items. Although effective for short-term prediction, the end-to-end generation paradigm inherently limits personalization. Its opaque decision-making process obscures holistic user profiling and exacerbates popularity bias. To address these limitations, we propose Preference Evolution Tracking (PET), a framework that reframes the task as inferring a dynamic probability distribution over a stable and interpretable lattice of preference clusters. By applying logit-probing and generative classification techniques, PET infers a user's preference as a probability distribution, enabling transparent preference learning. On public benchmarks (Yelp, MovieLens), PET improves ranking quality by up to 40% in NDCG over direct generation baselines. On a large-scale, real-world dataset from a short-video platform, it excels at ranking long-tail contents, significantly outperforming a SOTA production model by 7 times in the NDCG score. Ultimately, PET transforms the user profile model from direct preference list generation to a transparent distributional preference mapping, paving the way for more explainable, fair, and diverse personalization systems.