SPECTRA: Revealing the Full Spectrum of User Preferences via Distributional LLM Inference

Abstract

Large Language Models (LLMs) are increasingly used to model user preferences, with the typical output as a directly-generated ranked item list per user. However, this generative paradigm inherits the bias and opacity of autoregressive decoding. It over-emphasizes frequent (head) preferences and suppresses minority, long-tail ones. To address this, we propose SPECTRA (Softmax Probing for Extracted Category-level Token Readouts and Analysis), which treats the finetuned LLM as an implicit probabilistic model and probes its softmax to infer a probability distribution over semantically interpretable preference categories. We evaluate SPECTRA on MovieLens, Yelp, and a large-scale short-video platform. SPECTRA delivers (i) distributional alignment, reducing Jensen-Shannon divergence to the empirical preference distribution by 38 to 44 percent across public datasets; (ii) long-tail recovery with cross-user fairness, raising top-3 category exposure entropy by 23 percent on MovieLens and producing a larger gain on tail-preference users than on head-preference users; and (iii) downstream application value, with a 41 to 46 percent category-NDCG boost on MovieLens and Yelp, and a 7x improvement on long-tail category ranking on a large-scale deployment against a head-optimized production ranker.