OpenNovelty.org

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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Bayesian optimizationpreference elicitationautoencoderLLM
Abstract:

High-stakes, infrequent consumer decisions, such as housing selection, challenge conventional recommender systems due to sparse interaction signals, heterogeneous multi-criteria objectives, and high-dimensional feature spaces. This work presents an interactive preference elicitation framework that couples preferential Bayesian optimization (PBO) with two complementary components: (i) large language models (LLMs) that interpret natural language input to produce personalized probabilistic priors over feature utility weights to mitigate cold start, and (ii) an autoencoder (AE)-based latent representation that reduces effective dimensionality for sample-efficient exploration. The framework learns a latent utility function from user pairwise comparisons observed and integrated in real-time. We evaluate the developed method on rental real estate datasets from two major European cities. The results show that executing PBO in an 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 probabilistic weight priors that use LLMs only to rank feature importance achieve 25% better pairwise accuracy on average than a direct approach.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes an interactive preference elicitation framework that integrates preferential Bayesian optimization with autoencoder-based dimensionality reduction and LLM-generated priors, targeting high-stakes consumer decisions like housing selection. Within the taxonomy, it resides in the 'Latent Space and Bayesian Optimization for Preference Learning' leaf under 'Optimization-Based Interactive Preference Elicitation'. Notably, this leaf contains only the original paper itself, with no sibling papers identified, suggesting this specific combination of techniques represents a relatively sparse research direction within the broader field of interactive preference elicitation.

The taxonomy reveals three main branches: conversational natural language approaches, optimization-based methods, and feedback analysis techniques. The paper's parent branch ('Optimization-Based Interactive Preference Elicitation') includes sibling leaves focused on constructive configuration synthesis and multi-agent reinforcement learning, both addressing preference learning through formal optimization but in different application contexts. Neighboring branches explore conversational systems that use dialogue-based elicitation and knowledge-enhanced methods that augment sparse signals through external structured information, representing alternative strategies to the paper's model-driven latent space approach for handling sparsity and high dimensionality.

Among thirty candidates examined, the framework's integration of PBO, AE, and LLM-based priors shows no clear refutation across ten candidates reviewed. The autoencoder latent space execution for PBO examined ten candidates and identified one potentially overlapping prior work, suggesting some existing exploration of dimensionality reduction in preference optimization contexts. The LLM-based probabilistic prior generation component examined ten candidates with no refutable overlap, indicating this particular application of language models for cold-start mitigation in preference elicitation may be less explored within the limited search scope.

Based on the top-thirty semantic matches examined, the work appears to occupy a relatively novel position combining three distinct technical components for high-dimensional preference learning. The taxonomy structure and sibling paper distribution suggest the specific integration represents a sparse research direction, though the limited search scope means potentially relevant work in adjacent optimization or language model applications may exist beyond the candidates reviewed.

Taxonomy

Core-task Taxonomy Papers
9
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Interactive preference elicitation for high-dimensional sparse-feedback recommendation. This field addresses the challenge of learning user preferences when explicit feedback is scarce and item spaces are large, requiring systems to actively query users or leverage diverse signals. The taxonomy reveals three main branches. Conversational and Natural Language-Based Preference Elicitation encompasses methods that use dialogue and natural language interactions to gather user preferences, often employing conversational agents to iteratively refine recommendations through multi-turn exchanges, as seen in works like Neighboring Conversational Recommendation[1] and Conversational Recommender Systems[5]. Optimization-Based Interactive Preference Elicitation focuses on formal frameworks that model preference learning as an optimization problem, frequently employing Bayesian methods, latent space modeling, or active learning strategies to efficiently explore user preferences under uncertainty, exemplified by approaches such as Constructive Preference Elicitation[3]. Feedback Analysis and Knowledge-Enhanced Recommendation investigates how to extract richer signals from implicit and explicit feedback, sometimes integrating external knowledge graphs or structured data to mitigate sparsity, as illustrated by Implicit Explicit Feedback[7] and Feedback Knowledge Graph[8]. A particularly active line of work explores how to balance exploration and exploitation when feedback is sparse, with some methods emphasizing conversational turn-taking to reduce user burden (e.g., Adversarial Conversational Recommendation[6]) and others prioritizing principled uncertainty quantification in latent spaces. Interactive Latent Preference[0] sits within the Optimization-Based branch, specifically targeting Bayesian optimization in latent representations to handle high-dimensional item catalogs. Compared to conversational approaches like Neighboring Conversational Recommendation[1], which rely on natural language exchanges, Interactive Latent Preference[0] adopts a more model-driven strategy, learning compact latent embeddings to guide interactive queries. This contrasts with knowledge-enhanced methods such as Feedback Knowledge Graph[8], which augment sparse signals through external structured information rather than iterative user interaction. The central trade-off across these branches remains how to minimize user effort while maximizing preference information gain in sparse, high-dimensional settings.

Claimed Contributions

Interactive preference elicitation framework combining PBO, AE, and LLM-based priors

The authors propose a comprehensive framework that integrates preferential Bayesian optimization with autoencoder-based feature embeddings and LLM-based warm-start prior elicitation. This enables efficient preference learning in a low-dimensional latent space while users interact in the full-dimensional presentation space.

10 retrieved papers
Executing PBO in autoencoder latent space for high-dimensional feature spaces

The framework performs Bayesian optimization in the learned low-dimensional latent space of an autoencoder rather than the original high-dimensional feature space. This decouples the optimization space from the presentation space, improving convergence efficiency while maintaining representational resolution.

10 retrieved papers
Can Refute
LLM-based personalized probabilistic prior generation for cold-start mitigation

The authors introduce a method that uses LLMs to conduct automated user interviews, generating personalized probabilistic priors for initializing the preference model. Instead of directly specifying weights, the LLM ranks feature importance, which informs sampling from distributions to create uncertainty-aware priors.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Interactive preference elicitation framework combining PBO, AE, and LLM-based priors

The authors propose a comprehensive framework that integrates preferential Bayesian optimization with autoencoder-based feature embeddings and LLM-based warm-start prior elicitation. This enables efficient preference learning in a low-dimensional latent space while users interact in the full-dimensional presentation space.

Contribution

Executing PBO in autoencoder latent space for high-dimensional feature spaces

The framework performs Bayesian optimization in the learned low-dimensional latent space of an autoencoder rather than the original high-dimensional feature space. This decouples the optimization space from the presentation space, improving convergence efficiency while maintaining representational resolution.

Contribution

LLM-based personalized probabilistic prior generation for cold-start mitigation

The authors introduce a method that uses LLMs to conduct automated user interviews, generating personalized probabilistic priors for initializing the preference model. Instead of directly specifying weights, the LLM ranks feature importance, which informs sampling from distributions to create uncertainty-aware priors.

Table of Contents