CAPE：基于贝叶斯自适应提问的冷启动个性化偏好推断方法

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Abstract:Cold-start personalization requires inferring preferences from minimal interaction when no user-specific historical data is available. The space of possible preferences is vast, yet users care about only a sparse subset and rarely articulate them upfront; combined with limited interaction budgets, this makes preference elicitation challenging. Our key insight is that preferences exhibit predictable structure across populations; e.g., users who want detailed explanations often also value worked examples. We propose CAPE (Cold-start Adaptive Preference Elicitation), which learns a structured world model of preference correlations offline from complete profiles, then performs training-free Bayesian inference online to select informative questions and predict complete preference profiles, including dimensions never asked about. Even simple belief model instantiations (e.g., linear regression) substantially outperform end-to-end RL. Across medical, mathematical, social, and commonsense reasoning, CAPE achieves 80.8% alignment with ground-truth user preferences versus 68.5% for RL, requires 2.5x to over 15x fewer interactions, and adapts its questions twice as often as RL. Our contribution is a principled decomposition of cold-start personalization that makes Bayesian preference elicitation practical at scale for LLM systems.

Comments:	26 pages, 4 figures, 10 tables
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:	arXiv:2602.15012 [cs.CL]
	(or arXiv:2602.15012v2 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2602.15012 arXiv-issued DOI via DataCite

Submission history

From: Shuyue Stella Li [view email]
[v1] Mon, 16 Feb 2026 18:52:13 UTC (2,273 KB)
[v2] Tue, 10 Nov 2026 03:29:41 UTC (1,992 KB)