Paper detail

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

Large Language Models (LLMs) are increasingly used to understand user preferences, typically via the direct generation of ranked item lists. However, this end-to-end generative paradigm inherits the bias and opacity of autoregressive decoding, over-emphasizing frequent (head) preferences and obscure long-tail ones, thereby biasing personalization toward head preferences. To address this, we propose SPECTRA (Semantic Preference Extraction and Clustered TRAcking), which treats the LLM as an implicit probabilistic model by probing it to infer a probability distribution over interpretable preference clusters. In doing so, SPECTRA reframes user modeling from sequence generation with decoding heuristics to distributional inference, yielding explicit, cluster-level user preference representations. We evaluate SPECTRA on MovieLens, Yelp, and a large-scale short-video platform, demonstrating significant gains across three dimensions: SPECTRA achieves (i) distributional alignment, reducing Jensen-Shannon divergence to empirical distributions by 25% against strong baselines; (ii) long-tail exposure, reducing decoding-induced head concentration and increasing global exposure entropy by 30%; and (iii) downstream applications such as personalized ranking, translating these gains into a 40% NDCG boost on public datasets and a 7x improvement on ranking long-tail preferences against an industry-leading Transformer-based production baseline.