Supporting High-Stakes Decision Making Through Interactive Preference Elicitation in the Latent Space

High-stakes, infrequent consumer decisions, such as housing selection, challenge conventional recommender systems due to sparse interaction, heterogeneous multi-criteria objectives, and high-dimensional features. This work presents an interactive preference elicitation framework utilizing preferential Bayesian optimization (PBO) to learn the unknown utility function of a user from pairwise comparisons that are integrated in real-time. To increase efficiency in a complex feature space, we learn the preference model in the latent space of an autoencoder (AE). Additionally, to mitigate a cold start, we obtain a personalized probabilistic prior through an automated user interview with a large language model (LLM). We evaluate the developed method on rental real estate datasets from two major European cities. The results show that executing PBO in the AE latent space improves final pairwise ranking accuracy by 12%. For LLM-based preference prior generation, we find that direct, LLM-driven weight specification is outperformed by a static prior, while probabilistically weighted priors using LLMs achieve 25% better pairwise accuracy.