Supporting High-Stakes Decision Making Through Interactive Preference Elicitation in the Latent Space
Authors: Michael Eichelbeck
Tim Voigt
Matthias Althoff
Venue: ICLR 2026 Poster (2026)
Keywords: Bayesian optimization
preference elicitation
autoencoder
LLM
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
TL;DR: We learn housing preferences from pairwise comparisons using preferential Bayesian optimization in the latent space of an autoencoder.

Abstract: 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.

Published as a conference paper at ICLR 2026
SUPPORTING HIGH-STAKES DECISION MAKING THROUGH INTERACTIVE PREFERENCE ELICITATION IN THE LATENT SPACE
Michael Eichelbeck; Tim Voigt, Matthias Althoff
Department of Computer Engineering Technical University of Munich, Germany (mi chael 1 e che lbeck, toigt, althofffetum.de
ABSTRACT
High-stakes, infrequent consumer decisions, such as housing selection, chal- lenge 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 opti- mization (PBO) to learn the unknown utility function 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 im- proves final pairwise ranking accuracy by 12%. For L.-M- 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.
INTRODUCTION
User-tailored recommendations are a cornerstone of modern markets and online platforms, aiming to present the most relevant options to reduce decision paralysis and increase click-through rates. Traditional recommendation approaches excel entertainment or e-commerce domains, where user behavior generates abundant implicit feedback through clicks, purchases, and ratings. However, they struggle in sparse-feedback environments, where users interact with only a handful of options before committing to one, motivating strategies for eliciting preferences in interactive manner with as few interactions as possible. Such environments often correspond to high-stakes infrequent decisions characterized by complex heterogeneous calti-dimensional preference spaces; they have received little attention in the recommender system literature. We focus on the real estate market as an exemplary case study of an underexplored domain (Gharahighehi al. 2021).
1.1 RELATED WORK
Classical Preference Elicitation Traditional preference elicitation methods include conjoint anal- ysis for estimating utilities over multi-attribute items (Arora & Huber 2001) and multi-armed bandit approaches that balance exploration and exploitation (Parapar & Radlinski] (021). Recent multi- armed bandit extensions incorporate knowledge graphs to model inter-item relations and improve elicitation efficiency (Zhao er al., (202)). Preferential Bayesian optimization (PBO) adapts Bayesian optimization (BO) principles scenarios lacking explicit objective functions by using implicit feed- back like pairwise comparisons (González et al. 2017). Subsequent work has focused on developing acquisition functions accounting for uncertainty both model predictions and user responses (As-
tudillo & Frazier 2020:
Astudillo et al. 2023).
"Equal contribution

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Preference Elicitation with LLMs Preference elicitation based on large language models (LLMs) follows two main approaches. Conversational methods enable dynamic natural language dialogues, using methods like GATE to actively clicit user intent through open-ended interactions (Li et al. Andukuri et al., 2024). Structured approaches integrate L.LMs within probabilistic frame- works, combining language models with Bayesian methods. One type of structured elicitation uses LLMs for user interaction and Bayesian methods for maintaining preference beliefs (Handa et al., Austin et al. 2024). Similar approaches fine-tune LLMs in a supervised manner using prob- abilistic models, yielding improved conversational preference elicitators (Piriyakulkij et al. 2023 Qiu 2026). Here, Bayesian methods are only used for fine-tuning the model and do not directly aid in question selection and recommendation. Related work explores LLMs for decision support by constructing utility functions from stated user goals (Liu al. 2025). which does not incorporate Bayesian methods but relies on Monte Carlo simulations for expected utility maximization. Existing work has focused on discrete feature spaces, such as category labels. For feature spaces with several continuous dimensions, natural language representations are inefficient. To still leverage LLMs, propose pnddde conversations solely to generate personalized priors for downstream PBO tasks.
2025
2024
Bayesian Optimization in High-Dimensional Spaces Real-world recommendation scenarios fre- quently involve high-dimensional feature spaces that challenge conventional PBO approaches, as the search space grows exponentially with each additional dimension (Bellman) 1966). Two strategies address the curse of dimensionality in BO. The first explores lower-dimensional subspaces itera- tively via one-dimensional subspace exploration for high-dimensional PBOs (Tucker et al. 2020) Cheng et al.] 2020). The second strategy performs optimization in learned low- dindennnsional latent spaces. One approach uses a random projection matrix to embed the doubled dueling solution space into lower-dimensional preferential intrinsic subspace, where optimization is performed while pairwise comparisons remain in the original space, approximately preserving the preference func- tion (Zhang al., 2023). Another approach employs manifold Gaussian processes (GPs), i.c., GPs with a neural network embedded in the covariance function, to jointly learn a nonlinear feature mapping, the response surface, and a multi-output GP reconstruction mapping, enabling acquisition function optimization in the straint that encourages exploration near observed data (Moriconi al.) 2020). Similarly, variational autoencoders (AEs) have been used to learn low-dimensional latent spaces for BO, for instance in constrained molecular design, where the learned representation avoids invalid regions of the search space (Griffiths & Hernández-Lobudd Lobato, 2020). The ability of AEs to capture nonlinear dependencies through flexible compression makes them particularly suited for high-dimensional feature spaces with strong interdependencies. However, to the best of our knowledge, AEs have yet been plored for interactive preference elicitation.
feature space subject to Lipschitz continuity con-
1.2 CONTRIBUTIONS
This work addresses the challenge of efficiently learning user preferences in high-ddmensional, com- plex recommendation domains where direct preference specification is difficult, interaction data is sparse, and new data becomes available over time. We propose a comprehensive framework that couples PBO with user-specific warm-start prior elicitation through LLMs, and AE-based feature embeddings. This facilitates preference learning a low-dimensional latent space.
We evaluate our approach in the context of recommendations for renting real estate. While this serves as an example for challenging high-stakes domain, our approach generalizes to other do- mains with similar characteristics, such the automotive financial services markets. Through LLM-based and statistics- based user simulations, we demonstrate that our framework is real-time capable and outperforms vanilla PBO two real-world datasets of the real estate markets in Madrid, Spain, and Munich, Germany.
The remainder of this paper is organized as follows. After introducing some preliminaries (Sec. we pose our problem statement (Sec. [.I] and detail the proposed framework (Sec. 3.2 This is followed by the evaluation of our case study (Sec. 4) and the conclusion (Sec.
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2 PRELIMINARIES
Preference Learning Preference learning is concerned with learning models from empirical pref- erence data. preference can be conceptualized "relaxed constraint which, if necessary, can be violated to some degree" (Fürnkranz & Hüllermeier (2011). Common approaches range from ap- proximating individual utility functions to applying collaborative filtering across diverse user pop- ulations. Preference learning constitutes two primary problem types: learning utility functions and learning preference relations (Fürnkranz & Hüllermeier] (011). A typical task involves learning a function that predicts preferences for an unseen set of items, based on known set of preferences. This work focuses on the object ranking task. The objective to learn a function that produces a total ordering of of objects without access to explicit class labels form of unsupervised learning.
Bayesian Optimization BO provides a sample-efficient framework for global optimization of ex- pensive, black-box functions. It fits probabilistic surrogate the unknown objective and uses an acquisition function to decide what to evaluate next, balancing exploration and exploitation in a principled way (Frazier 2018). In this work, BO maximizes unknown function f :: - R over a compact feature space X' C Rdd At iteration k, we observe noisy evaluations
yk = f(xxk) + Ek, Ek ~ ~00,)))
and collect observations {(x,, ((rs,,)) W A common surrogate for the black-box function is GP prior, which yields Gaussian posterior any conditional on the observations collected far. The subsequent evaluation maximizes an acquisition function (x (z) that quantifies the value of sampling æ. These acquisitions should be cheap to evaluate, and several options have been proposed in the literature (Brochu et al. [2100] Astudillo et al. 2023; González et al. 2017). BO alternates between updating the surrogate with the new observation, maximizing (x) to choose 2k+1, evaluating 11+11 and augmenting the data. It terminates upon budget exhaustion or convergence, e.g., vanishing expected improvement.
Preference Bayesian Optimization PBO has the same objective as BO but operates under the assumption that direct querying of f is infeasible, so we have to rely on pairwise comparisons with two objects (Ia, 2b), so-called duels. In each duel, we receive binary feedback, indicating which object was selected. This dueling process is repeated until the uncertainty is reduced satisfying amount. Utilizing BO techniques reduces the number of duels needed, and utilizing a trained PBO model enables ranking of previously unseen items (González et al. 2017).
Autoencoders AEs are neural networks that learn compact latent representations by training an encoder Go Rd > R" and a decoder he IR" > Rd to reconstruct inputs, where r < the so-called latent dimension (Hinton & Salakhutdinoy (006). Training minimizes reconstruction loss, such as mean squared error for continuous features or binary cross-entropy for binary features, often with regularization (c.g., weight decay). A well-trained encoder idcally removes correlated features, captures nonlinear relationships, and distills the input into its most relevant components.
3 INTERACTIVE PREFERENCE ELICITATION FRAMEWORK
In this section, we present our problem statement and describe our approach in detail.
3.1 PROBLEM STATEMENT
We define X + R as the unknown utility function of a user, defined on the feature space X' C Rdd corresponding pairwise preference function
{ if a(x) u(x'),
F. Fx,xx')
(1)
otherwise
maps any pair of data points (z, x') to a binary response, indicating which option is preferable. Our goal is to obtain utility surrogate i" with corresponding pairwise preference probability
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distribution Fu, such that
i' = arg min "(z,a") [ (FF((1, Fu Fa(,,
(2)
where (x, .²) is pair drawn uniformly at random from X² and L is an appropriate loss function. While set of items
{.1,.... ) is known the time of preference elicitation, new op- tions might be unveiled over time. We assume a channel through which we can query the user by proposing pairwise comparisons and obtaining binary feedback. In realistic setting, the number of queries is limited by an unknown budget N N. Therefore, we aim to model the utility function of the user as accurately as possible with the fewest queries possible.
3.2 PREFERENCE BAYESIAN OPTIMIZATION IN THE LATENT SPACE
Our approach leverages AEs by performing BO in the latent space of the AE. Optimization in this reduced space should converge more rapidly while still capturing user preferences accurately. In summary, learn utility surrogate ú: Z + IR in the latent space Z C R' of the AE, with the corresponding pairwise preference function
if ((go(x)) (g96()
Fa(x, -
otherwise,
where go the encoder of the AE trained on the set of available items I. The associated encoder denoted hg. In addition to the following textual description, approach is formalized in Algorithm
Algorithm Preferential Bayesian Optimization in the Latent Space
(fi,...,,II1) } J. where x; E X C Rd Require: Encoder go X' Z, decoder he
Require: Item dataset = {I1, ..,,...,,
X, where Z C R' and < d
Require: Initialization budget M E N. query budget N E N
Ensure: Learned utility surrogate
+ R
Elicit user-specific feature weights and bounds:
(T, 2, x) + runl.LMConversation))
8² + caldFeetuureVarrincees/] Variances(Z)
w f
Initialize model:
3.2.1
D 5- /
> Set of observations based on pairwise comparisons Synthetic linear utility in feature space
Uinn (I:) +- TTx
for E (1....,M do
Sample random pair (22, x from I
if Unin (xx) > (in(1() then
Uh
else
Vks + end if
2$ + go (x(x)), 2/ +- go(x%)
D. Encode from original to latent space
D DU [(Ek 24 yb)}
end for as =Fi D)
Fit initial GP model with uninformative prior Úg
Interactive elicitation:
2.2
for & (M 1...., M N) do
Active candidate selection:
(zk, 2 <- arg max 2, (lgot (o(z) {EUBO,
Query user:
(Ih, x²,,) + (be(2)), ho ho(2))
Decode from latent feature space
y/ + getUserResponse (1. 2%))
Update model:
Ug.. = Fit((x-11 {(zx. 2/. Mk.) end for return ÚM4N
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3.2.1 UTILITY PRIOR ESTIMATION USING LLMS
In PBO, selecting informative duels is particularly important during the early stages of elicitation (Handa et al., 2024; Brochu et al. 2010), and an unsuitable starting point could waste valuable query budget. To mitigate this cold start, we aim to find maximally informative prior to initialize the preference model. This is achieved by evaluating M pairwise preference decisions based on a linear utility function (x)n
T x, where the prior weights w E [-1, ||w]|||| = are obtained through an LLM--uuded user interview instead of relying on a predefined static weight vector. Our approach builds on the initialization phase of (Handa et al. (024), extended here to handle continuous features.
User Interview The LLM is assigned the persona of domain-specift interviewer. Apart from reaching the query budget, the conversation can also conclude when the LLM determines it has gathered sufficient information or when the user wishes to stop the interview. The obtained pref- erence information either directly contains the utility surrogate weights the probabilistic initialization explained below. Additionally, lower and upper bounds g, 7 of the feature subspace that is acceptable for the user are returned by the LLM. Including hard constraints can make the elicitation process significantly more efficient by ensuring that all presented compar- isons fall within the acceptable feature subspace of the user. The used prompts are provided in the Appendix (Sec. A.2.2). An example output of the LLM for the real estate domain is:
ranking for
1. Lower bounds on essential criteria, including the minimum floor level, required living area in square meters, and available parking space.
2. Upper bounds for constraining criteria, such as maximum acceptable total monthly rent and maximum acceptable travel time to the workplace.
3. Feature importance weights representing the relative significance of each feature in the decision making process. The LLM is prompted to estimate these weights based on the conversation.
Probabilistic Weight Initialization Instead of directly returning utility function weights, we em- ploy approach based on the work in (Handa et al. 2024), which asks the LLM to rank features in order of importance a task that aligns better with the strengths of LLMs in comparative reasoning and ordinal relationships (Liusie et al.) [024 Liu et al.) 2024). This approach works by sampling feature weights from normal distributions whose parameters are informed by both the ranking of the I.LM and the empirical feature variances. For each feature with rank r; (where lower ranks indicate higher importance), the weight ue is sampled from:
Wi ~ N jo (39) 1))
(4)
where of represents the empirical variance of feature i before normalization. The intuition behind this approach that features deemed more important by the user (receiving lower rank values) should have larger absolute weight values, while features with higher empirical variance already exhibit significant influence on the feature space and thus warrant proportionally scaled weights. Different from (Handa et al. (2024), we add (o..) as normalization term ensuring that
features with exceptionally large variances do not receive disproportionately large weights regardless of their actual importance to the user. The corresponding prompt for our case study provided in the Appendix (Sec. A.2.3).
Warm-Start Dataset
After the weights for the linear model Ugjn have been determined, sample M item pairs from I uniformly at random. For each pair (Ix, J we evaluate the associated pairwise preference function obtain the binary feedback yı Fun (Ik, Since the preference model is trained in the latent space, we compute the dataset of embedded observations
D (oo(#x), ge(2()) mk) k=o
as well as approximate embedded lower and upper bounds of the feasible feature subspace = go (z), 2 (z) Note that since the encoder go is not necessarily monotone, these bounds rep- resent an approximation of the feasible region in latent space. In practice, we observe that this approximation works well for the learned encoders in our experiments.
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3.2.2 ELICITATION LOOP
Denoting the hthh update of the utility surrogate û based on new observation data B as 0R Fit(Ox-1. B), we initialize à using the warm-start dataset as üM Fit Fuo D). where ùg is ini- tialized with an uninformative GP prior. From hereon, our approach follows the principle of PBO. Until the query budget N is reached, we determine each new query (zR, #f)) by maximizing an acqui- sition function (*k, =/.). The user is shown the decoded query (#x(= ho (=(6) and their prefer- ence choice yi is recorded, and the preference model updated as uig Fit (1 [(2g, 2/.- me))) In the following two paragraphs, we describe the utility surrogate update and the acquisition function optimization in more detail.
Utility Surrogate Update When a user expresses a preference for an item & over H', we inter- pret this as evidence that ú(z) > ú(=²) and model the likelihood of this preference using a probit function:
Pr(x x') [((2) a(z')
(6)
where a captures user preference inconsistency as well as noise from the AE reconstruction error, and Φ is the cumulative distribution function of standard normal distribution. This follows the approach from (Chu & Ghahramani] 2005) with the difference that we learn our model in the latent space. We discuss the theoretical basis for our noise model the Appendix (Sec. [A.]]. The resulting
posterior distribution is not analytically tractable since the probit likelihood is non-conjugate with
the Gaussian process prior. We adopt the GP model from (Chu & Ghahramani] (2005) because natively handles pairwise preference observations, enables efficient posterior updates via Laplace approximation, and integrates naturally with the acquisition function used our elicitation loop.
Acquisition Function Optimization BO, each sample, our case a user query, is typically determined by acquisition function, optimizing the value gained through the corresponding ob- servation. For our approach, we choose the expected utility of the best option (qEUBO) acquisition function, summarized here for the sake of completeness. qEUBO is defined (Astudillo et al., 2023 Sec. 4.
(EUBO, Ex max ((=z),((z)))
where E, denotes the conditional expectation given our observations of user preference choices after k queries. Since û is modeled Gaussian distribution, qEUBO, can be efficiently maximized via a single-sample approximation (Lin et al. [2022] Sec. 4.3). While this, in principle, supports the
integration of arbitrary feature space constraints (Balandat al., 2020),, restrict ourselves to fcature-wise lower and upper bounds = E [2, v| that can efficienlly be extracted during our LLM- based prior estimation (see Sec. 3.2.1p.
3.3 EXTENSION FOR CONTINUAL AE IMPROVEMENT
When new items become available over time, we may want to leverage the opportunity to retrain and improve the used AE with an expanded input dataset. We outline a corresponding continual approach as follows: Consider the trained AE with encoder go and decoder hgg initially trained on set of items I. During elicitation, the AE generates a user-feedback dataset D, = ((20, *6= .... for the construction of the utility surrogate úg. When training encoder ga- and decoder hae on an expanded dataset I 5 I. To avoid losing previously collected feedback, re-embed the user-feedback dataset by decoding the latent representations through he and re-encoding them with the updated encoder goo: Dgo = {goo (ho(2u), goo (he (z))), yu).... This re-embedded dataset enables to rerun the PBO, yielding updated utility surrogate úg..
AE, we obtain updated
EVALUATION
In this section, we introduce the used datasets, explain experiment setup, and present our results.

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Neighborhoods
City Center
Main Streets
Metro
Park
Shopping
Culture
Restaurant
Figure 1: Visualization of city boundaries and points of interest for sample city.
4.1 DATASETS
We evaluated our method using the Idealistal8 open-source real-estate dataset (Rey-Blance al.) 2024). It comprises geo-referenced data of residential real-estate listings from the year 2018 for Spain's three largest cities Madrid (94,815 listings), Barcelona (61,486), and Valencia (33,622).
Each listing is accompanied by property attributes (e.g. price, unit price, number of rooms/baths, constructed area, presence of a terrace, lift, pool, garden, etc.), spatial coordinates (lati- tude/longitude, slightly perturbed to protect exact addresses), and supplemental data drawn from cadastral records (building quality, construction year, dwelling counts, etc.). The dataset also in- cludes neighborhood polygons for each city with official boundaries and set of points of interest per city: coordinates of the city center, main streets, and metro stations (cf. Figure For the sake of this evaluation, utilized all Madrid listings with manual selection of 12 features, focusing on property attributes. A detailed overview is given in Appendix A..] (Table 5 All analyses and created
results presented in Section 4.3 are based on this publicly available dataset. In addition, a comparable dataset for the city of Munich, Germany. It contains about 1,500 listings of rental real-estate with their corresponding metadata, alongside textual descriptions. Additionally, we uti- lized geospatial analysis to compute additional information, such proximity to the nearest public transport stop or the average surrounding noise level. While we are unable to publish dataset at this point due to licensing restrictions, report our evaluation results in Appendix A.3) Table
Notably, these results are in line with the findings reported in Section 4.3
4.2
SETUP
AE Training We employed robust scaling techniques that use interquartile ranges rather than mean and standard deviation, making the normalization less sensitive to outliers. Additionally, me- dian value imputation handles missing or malformed values, and outliers were removed by clipping the data at the 1st and 99th percentiles. The tuned architecture has two hidden layers in both the encoder and decoder and six latent features. We used hyperbolic tangent (tanh) as the activation function. The full set of hyperparameters is provided in Appendix [A.4] Table 9 The autoencoder trained with normalized features, which why we apply normalization before passing data encoder and denormalization before displaying decoded item to the user. For clarity, we do not include these steps in our formalization.
User Simulation Generating responses that approximate human preferences well is challenging task. Specifically for content recommendation, it was found that biographical sketches of hypo- thetical people are well-suited for simulating human decision makers (Li et al. 2025) with LLMs. Based on these findings, we prompted a state-of-the-art LLM with thinking capabilities to deter- mine which of the two properties, represented by textual representation of their feature vectors, prefers. Our primary evaluation used the closed-source model Gemini
and
https:/ googl leapis. s..pss.nn www.cooc///////o comm/eepniie nd-medi MMde1 - /ara - lash-liite-Moee -Cardd.ppe-add.offf

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conducted ablation experiment using the open-source model gpt-oss-1203 OpenAl et al. (025). The utilized prompt is provided in Appendix A.2.3
In addition to simulating human responses using LLMs, we implemented an approach based linear utility function model Üpm. We used a range of preset profiles, which were then randomized using uniformly sampled offsets, between -0.5 and 0.5, added each specified weight. We used the obtained model to make pairwise comparison decisions based on the Bradley-Tere Terry model of human preferences (van Berkum 1997] Hunter 2004). Accordingly, the likelihood of property being preferred over a property x is defined by
Pr(x x' (iiin + el e( -
The statistical profiles and LLM personas were chosen such that they roughly represent the same preferences and tendencies. For example, the budget-conscious profile and the student persona encode the same preference for an ideally low rent and proximity to the city center. Personas and profiles were derived from survey studies Walker & Li (2007): Lee et al. (2019) and were confirmed by domain experts to be relevant classes of stakeholders in the rental real estate market. The detailed weights and persona prompts are provided in the Appendix (A.2.4). The static prior is hand-crafted to represent reasonable preference profile and provided in Table
Evaluation Parameters For all evaluations, we used test set of n = 50 randomly sampled items from our dataset as ground truth, reused across all runs of the same persona or profile. The learned utility surrogate has no access to the test set, since we only use the posterior of the model for evaluation. Each result was based on 200 evaluation runs, evenly split between the LLM-bbaee and statistics-based user simulations, resulting in 100 runs each. Within each simulation type, runs were distributed equally across four personas (or profiles), yielding 25 runs per persona or profile. For one evaluation run, we selected an initialization budget of M 5 and query budget of N 25. Additionally, we tested a totally random prediction strategy to establish a baseline.
We used two metries to measure the performance of the elicitation methods. First, calculated the pairwise accuracy, which is the fraction correctly ordered pairs between the predicted and ground-truth preferences. Secondly, measure ranking quality that gives more weight to items ranked higher in the list Järvelin & Kekäläinen (2002). To define it, first introduce the discounted cumulative gain (DCG) at position k:
employed normalized discounted cumulative gain (NDCG),
a
rel:
DCG@k =
(8)
Loga 1)
where rel, is the relevance score of the item at position in the predicted ranking. DCG@K is nor- malized by the ideal discounted cumulative gain (DCG@@)) which is the DCG score of perfectly sorted list, to obtain
NDCG@k DCG@k
IDCG@K
Essentially, NDCG@K measures how much of the maximum possible utility was captured in the top & positions, relative to an ideal ranking for that query. We generally report the mean and 95% parametric confidence intervals across all runs.
4.3 RESULTS
Figure visualizes the performance differences between our proposed approach (combining PBO with AES and LLMs), the random ranking baseline, and vanilla PBO. PBO runs with statistical simulation achieved higher initial scores but experienced rapid decline after a few iterations. This strong initial performance was likely the result of overlap between profile weights and the default static prior used in evaluation. Under LLM-based simulation with noisier signals, our approach consistently outperformed vanilla PBO. Our method achieved average final pairwise accuracy of 0.613 + 0.024 and an average NDCG@ 10 score of 0.706 = 0.034. These results represent vanilla PBO under LLM-based simulation, respectively. A
13.7% and 13.5% improvements noteworthy observation in Figure that vanilla PBO in the statistical simulation showed the coun- terintuitive behavior of initial rapid increase in accuracy followed by monotone decrease until
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0.70
0.8
0.63
0.7
Pairwie Accuracy
0.60
NDCGGR1
0.55
0.6
0.50
0.5
00.45
0.40
Ours (LLM)
Vasilla (LL.M)
0.4
Cuurs (LLM)
Vanilla (LLM)
(Statittiaa)
(Statistica))
(Statiscica)
10
30
20
Queries
Queries
(a) Aggregate pairwise ranking accuracy.
(b) Aggregate NDCG@ scores.
Figure 2: Aggregated scores over time for random ranking, vanilla PBO (both user simulation types), and our proposed approach. Shaded areas represent 95% confidence intervals over 200 runs each.
0.20
0.8
0.65
0.7
Pairwise Accuracy
NDCCR1D
0.55
0.6
0.50
0.5
0.55
Direct Prioe
Direct Prior
0.40
Price
Probebilatic
Static Prior
Static Ptior
0.35
0.3
10
5
10
1.5
20
Queries
Queries
(a) Prior comparison for pairwise ranking accuracy.
(b) Prior comparison for NDCG@ 10.
Figure 3: Comparison of PBO+AE performance using three different prior initialization methods. The probabilistic LLM-based prior slightly outperforms the static prior, while the direct LLM-based prior yields the worst results.
the end of the elicitation loop. This indicates overfitting certain region of the feature space, which is likely a consequence of the high dimensionality leading to premature exploitation.
The performance improvement incurred an average overhead of 358ms per optimization step com- pared to vanilla PBO. Additionally, we measured candidate diversity ensure the decoder output does not collapse to similar objects. We define candidate diversity as the mean feature-wise stan- dard deviations across candidates generated during acquisition function optimization, measured in the feature space. We observed no significant difference between PBO and PBO+АE methods. Table presentss the detailed aggregated evaluation results.
similar performance pattern emerged when applying our method to the Munich rental real estate dataset (Appendix A.3 Tables
7 Although the pairwise ranking accuracy slightly lower this smaller dataset, the NDCG 10 scores are comparable. Crucially, PBO+AE again demonstrated better predictive performance
vanilla PBO. This suggests that our approach effectively learns preferences even for high-dimensional datasets of a smaller size. Additionally, we provide the ag- gregated results from utilizing the open-source LLM in Appendix A.33 Table g While we observed generally worse performance of all variants compared to the closed-source LLM, our approach still outperformed vanilla PBO. Further, the usefulness of warm starting demonstrated in Appendix Figure 5 which showed that PBO+AE with cold start quickly plateaued and performed worse than our approach the end of the elicitation process.
A.3
LLM Prior Impact Figure shows the results of ablation study over all three initialization strategies for PBO+AE: a fixed static prior, a directly elicited LLM prior (point estimate), and a probabilistically elicited L.M prior that samples weights from distribution informed by an L.M--

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Table 1: Comparison of evaluation metries across all approaches and user simulation variants.
Method
Simulation
Prior
Pairwise Acc.
NDCG@10 10
Cand. Diversity
Runtime/iter (ms)
PBO
LLM
Static
0.539 0.014
0.622 : 0.026
0.775 0.116
518 + 10
Statistical
Random
0.492 = 0.017
0.489 + 0.038
1.078 0.040
0
Static
0,510 0.017
0.658 = 0.037
0.633 0.060
304 = 12
PBO + AE
L.M
Direct Elicit
0.488 # 0.024
0.573 + 0.036
0.664 # 0.057
641 + 655
Prob Elicit
0.613 # 0.024
0.706 # 0.034
0.596 # 0.066
876 + 216
Static
0.605 * 0.024
0.685 * 0.033
0.611 0.064
723 + 99
Statistical
Static
0.556 0.025
0.584 a 0.037
0.613 0.039
465 + 84
produced feature ranking (Sec. (.2.1]. The first five queries used synthetic comparisons generated under the respective prior (vertical marker), after which the model observed simulated user feed- back. Feature- bounds were active and identical across the LLM-based variants. The static prior runs used wider dataset-level bounds instead. Across 200 runs for all personas, the probabilis- tic prior yielded the best sample efficiency and the highest final performance on pairwise accuracy and NDCG@ 10. The direct prior showed early drop consistent with overconfident misspecifi- cation - and never closed the gap. At the query budget limit, PBO+AE with probabilistic elicitation achieved pairwise accuracy of 0.613 + 0.024 and NDCG@ 10 of 0.706 = 0.034, slightly but consistently outperforming the static prior and clearly surpassing the direct prior. These results indicated that an uncertainty-aware prior based LLM guidance is sustained advantage once real user feedback arrives. We hypothesize that the static prior shows comparatively strong results because it is likely good fit for most personas. For example, the rel- atively strong preference for a lower price encoded in the static weight prior is likely to match the preferences of every persona. This effect is unlikely to generalize larger population of users.
robust and provides
4.4 LIMITATIONS
Our evaluation has several limitations. The LLM-based personas used in our simulations may not accurately reflect authentic human decision- makiing and they represent only a limited number of stereotypical users. LLM responses not fully consistent across queries, even with low tempera- ture settings. Our datasets are from two major European cities, which limits their generalizability to other geographic markets cultural contexts. The selected features (e.g., bikeability scores, public transport access) reflect local urban characteristics that may not be applicable to different settings or recommendation domains, such as automotive purchases. Additionally, our reliance on pairwise accuracy as the primary evaluation metric may not fully capture user satisfaction, as real users often value factors beyond ranking accuracy, such as diversity or novelty.
CONCLUSION
This work demonstrates that combining preferential Bayesian optimization with LLM-guided pri- ors and autoencoder-baste dimensionality reduction effectively addresses preference learning chal- lenges in high-stakes, pparse-interaction domains. We have achieved substantial accuracy improve- ments compared to vanilla preferential Bayesian optimization on rental market datasets from two European cities. Our framework has immediate applications for online real estate platforms, where it could reduce user fatigue by minimizing the number of property comparisons needed to identify suitable options. Beyond rental real estate, further real- wol-world applications are high-stakes decisions, e.g., job searches or major purchases, where sparse interaction data limits traditional recommender systems. Key directions for future work include multi staeii-stakeooldrr preference aggregation (e.g., couples searching together), temporal adaptation for evolving preferences, investigation of other decision domains, and empirical validation with human users.
ACKNOWLEDGEMENTS
We gratefully acknowledge funding by the German Research Foundation (grant no. 458030766).
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A APPENDIX
A.1 MOTIVATION OF RECONSTRUCTED PREFERENCE LIKELIHOOD
We would like stress that the utility model in equation 6 not require any assumptions on the utility function of the user or the AE accuracy. If the underlying noise from user preferences and AE reconstruction errors is not Gaussian, we may obtain biased or less accurate model, which, how- ever, may still perform well on ranking tasks. Subsequently, we discuss under which assumptions the noise introduced by learning the latent space instead of the feature space can be modeled as being absorbed in distribution learned in the feature space.
We make two assumptions. First, the AE reconstruction error can be modeled as unbiased Gaussian noise, i..., - (g)((z)) well-trained model. Second, the reconstruction error locally affects the utility function of the user approximately linearly, such that
+ where (0, ).). This should be the case for sufficiently
u (x))
u (
This first-order approximation is accurate when the reconstruction error is small relative to the cur- vature of u; for large errors, higher-order terms may become significant. The technique lineariz- ing input noise obtain effective output noise in GPs was introduced by McHutchon & Rasmussen (2011 Eq. 4-6). If we did not use any embedding mechanism, the preference likelihood would be modeled directly (Chu & Ghahramani
2005 Sec. 2.1.2)
Pr(x > x' u) +b ( (u(x) -u(u(()))
Opeel
where pret represents the intrinsic preference noise. To establish the connection to the latent space, we apply the first-order Taylor expansion around two items x and x' and obtain
u(x) - u(=')= = u($) - u(t') - Vu( ++ Vu(E')
The noise term n( -') Vu(())=-Vu(t')
depends on the local gradients Vu(E) and Vu()'). Conditional on & and H, it is linear combination of independent Gaussian variables. such that n (0, of reg (ě, E))) with variance 0 (') T Vu(#)= + T Vu(ě'). For the sake of computational efficiency, we regard the varying conditional variance as a constant o2 ooson RR (o (#, d')], yielding 7 N(0, Rooo)) making possible approximate the
preference likelihood as follows:
Pr(x > d' û) Pr(u(r) - u(x') > 0)
2 Pr([u(#) u((') - 17 >
Pr((u(ho(=)) u(hh(=())) - 1 0)
a(*)
0(2))
((>)) ((()))(())))
where (i) applies the first-order Taylor expansion and the constant-variance approximation of 1,, (ii) substitutes å h=(2), i.e., ((z) = u(ha(z)), and (iii) follows from 1) ~ NN oz combined with the preference noise, yielding the total observation noise o² = p + T
Approximating the input-dependent variance as constant is the core simplification of the above argument. We support the validity of this step by empirically investigating how the reconstruction error of an item depends on its position the feature space. Figure shows the reconstruction error of each data point across two principal components of the feature space after principal component analysis. We observe that the error remains rather constant over a wide range of the data, with higher emrors primarily occurring near the edges of the feature space. A more sophisticated model of PBO explicitly considering input- input-dependent noise has recently been explored by [Sinaga et al. (024)) who model heteroscedastic preference noise stemming from varying levels of human aleatoric un- certainty. Extending such models to additionally capture reconstrucciooninddeee heteroscedasticity offers interesting possibilities for future work.
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1.0
iosonstmrction Error (MSE)
PC2 (23.6% var.)
0.5
0.8
0.0
0.6
-0.5
0.2
PC1 (36.5% var.)
Figure 4:
Autoencoder reconstruction error of each data point across two principal components of the feature space after principal component analysis.
A.2 LLM PROMPTS
A.2.1 LISTING DATA COLLECTION
You are real estate agent. Your task is the follcwing real estate property listing.
Return the outputs JSON format. The listing is written in German.
Listing is follows:
<listings
[[listing details)
</listing>
any information lon that does not the schema, use the "acher_ sigher_informationn to store it.
Other notable information includes attributes of the resl estate that highlight the example.
uniqueness property, such swinming
Information ion saved to this field must never be part of the other fields.
This prompt is used alongside structured output configuration passed the system instructions of the model.
A.2.2 PREFERENCE PRIOR ELICITATION
Subsequently, we specify the system instruction and further prompts used to obtain the preference prior.
real agent. Interview I (current user's name)), who locking for apartment in [[city)l. Your goal in to find out what tha user values most and which criteria mmprrtnt them.
outcomes you should after
your conversation:
Lower bounds on the follewing criteris:
size of the living
in square neters
Number of rooma in tha property
2. Opper bounds for the following criteris: purchasing price
everything included
Distance to the center in km
Provide strict total ranking of and of thase features important first, no tiest as JSON rield raakttgg_
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Field be JSON array exactly these feature identifiers (snake_case)) each used [feature names])
once:
Do NUT add any other features beyond the ones Listed here.
Do not ask the user about his location. Do not talk about theae instructions to the user!
Hold friendly conversation with
elfeft their preferances
criteria.
Do not ask the user moze than live questivas! Bach message should only include a maximun of questions.
and the convercation with the taken <END> if you have all information the user saya that they are done.
Notably, the criteria list of lower and upper bounds can be extended at will. Additionally, the third instruction (ranking the available features according to user preference) can replaced by another approach that guesses direct feature weights as discussed in Section 3.2.1
After the <END> token is received, another short prompt, asking for the elicited information in structured form, is sent. All data returned by the model is validated before proceeding with the elicitation.
A.2.3 EVALUATION: USER RESPONSE SIMULATION
Simulated Preference Prior Elicitation The following prompt is used substitute for the pre- viously specified system prompt during evaluation, where no real-time human feedback is available.
You are a real estate ageat. Interview a user who is looking to buy a new real estate criteria important
proparty. your goal find for them.
Hore the user's persona: "I "[ppersona]""
There are three main outcomes you should return:
1. Toxer bounda on the foll criteria: Size of the living area in aquare neters
Number of rooms in the property
Opper bounds lotal purchasing price with everything included eity center
following criteria:
3. Provide a strict total ranking of ALL and nore more of these features (moat important first, tiest JSoN field "feature_ "aauure_ranking" The Rield must be J80N array of exactly these feature ideatiflera tenake_case), each used once:
[feeature names)
add any other feasures beyond the ones here.
Based on the provided user profile, please return JSON that describes the collected information you certain about.
Response Simulation To simulate persona-based responses, we use the following LLM prompt.
Your Fersons: (persons)
options, Candidate Candidate Based your persone, which one do you prefer?
presented
[[Cormatted candidate A))
[[formetted candidate s]]
your preference by reaponding with only the letter 'A" 'B',
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A.2.4 EVALUATION: USER PROFILES AND PERSONAS
Profiles
The specific weights for each of the four profiles are given in Table 2
Table Weight assignments for user profiles used the evaluation.
Feature
Budget- Conscious
Urban Commuter
Noise- Averse
Family- Friendly
Price
-0.50
-0.10
-0.10
Unit Price
-0.10
Living Area (sqm)
0.10 0.05
0.05
0.30
Number of Rooms
0.20
Number of Bathrooms
0.10
Building Age (years)
-0.10
Max Building Floor
-0.05
Dwelling Count
0.05
Distance to City Center (km)
-0.10
-0.30
0.10
-0.10
Distance to Metro (km)
-0.20
-0.30
-0,20
Distance to Castellana (km)
-0.05
0.40
0.05
Personas
The persona prompts used for LLM-based user simulation are given in Table
Table 3: Persona prompts used for LLM-based user simulation.
Persona
Prompt
Family
You the head of family with two young children. You prioritize space, multiple rooms and bathrooms, and high-qualig, housing. You value prop- erties with more floors in the building for better amenities. You can afford higher prices but want good value per square meter. Distance to city center is less important than living space.
Student
You are university student on a tight budget. Low price is your absolute top priority, and you't 're willing to accept smaller space and fewer rooms. You prefer being close to the city center and metro stations for easy access to uni- versity and nightlife. You don't 't mind older buildings if it means lower costs.
Young Professional
You are young professional who values convenience and modern living. You prioritize proximity to metro stations and reasonable distance to city center for your commute. You prefer newer buildings with good quality, and you'l willing to pay higher prices per square meter for better location and quality. Moderate space requirements are sufficient.
Noise-Averse
You prioritize peaceful living and prefer properties farther from the busy city center and metro stations to avoid noise. You value higher floors in buildings for reduced street noise, and you're 're willing to pay premium prices for tranquil locations. Living area size is important, but distance from transportation hubs is preferred for quieter environment.
The static weight prior for model initialization, used in runs without LLM- based weight initialization, is provided in Table
Statie Weight Prior
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Table 4: Static weight prior for model initialization, used with the Idealista18 dataset.
Feature
Weight
Total Rent
-0.30
Unit Price
0.00
Constructed Area (sqm)
0.20
Number of Rooms
0.10
Number Bathrooms
0.05
Building Age
-0.10
Max Building Floors
0.01
Dwelling Count
-0.01
Distance to City Center
-0.10
Distance to Metro
-0.10
Distance to Castellana
-0.03
Cadastral Quality
0.00
A.3 EVALUATION
Idealista Dataset Table describes the subset of the Idealistal8 dataset that we use to evaluate
proposed approach.
Table 5: Overview of the 12 selected columns from the Idealistal8 88 dataset.
Count
Mean
Std
Min
Max
Price [C]
94815.00
396110. 10.11
417074.41
21000.00
8133000.00
Unit Price [E/m²]
94815.00
3661.05
1700.50
805.31
9997.56
Constructed Area [m²]
94815.00
101.40
67.08
21.00
985.00
Number of Rooms
94815.00
2.58
1.24
0.00
93.00
Number of Bathrooms
94815.00
1.59
0.84
0.00
20.00
Age [y]
94815.00
59.30
29.11
7.00
402.00
Max Building Floor
94815.00
6.38
2.85
0.00
26.00
Dwelling Count
94815.00
39.19
54.25
1.00
1499.00
Distance To City Center [km]
94815.00
4.49
2.99
0.01
415,75
Distance To Metro [km]
94815.00
0.48
1.43
0.00
399.48
Distance To Castellana [km]
94815.00
2.68
2.58
0.00
412.80
Cadastral Quality ID
94815.00
4.85
1.46
0.00
9.00
Munich Dataset While we are currently unable to publish the complete dataset for the Munich
metropolitan region, we describe the most relevant statistics in Table 6 All observed real estate properties were offered for rent. Travel time was calculated using the open-source OTP router with preference to walking for shorter distances and public transport for longer distances. Scores were determined based on custom geospatial scoring framework.
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Table Overview of our custom Munich dataset.
Count
Mean
Std
Min
Max
Total Rent [E]
1561.00
1753.86
853.
29.00
13900.00
Floor
1561.00
2.32
2.03
0.00
17.00
Living Area [m²]
1561.00
61.12
30.77
10.00
270.00
Parking Spaces
1561.00
0.43
1.39
0.00
8.00
Outdoor Leisure Score
1561.00
0.40
0.05
0.25
0.98
Recreation Dining Score
1561.00
0.53
0.07
0.00
0.79
Bikeability Score
1561.00
0.58
0.21
0.00
1.00
Noise Score
1561.00
0.92
0.17
0.20
1.00
Safety Score
1561.00
0.93
0.14
0.00
1.00
Travel Time to Public Transport [s]
1561.00
211.89
113.23
1.00
671.00
Travel Time to Grocery Store [s]
1561.00
309.62
180.47
2.00
896.00
Travel Time to Outdoor Leisure [s]
1561.00
333.77
173.38
2.00
1009.00
Travel Time to City Center [s]
1561.00
1690.39
759.04
311.00
3979.00
Table 7 shows the results obtained after evaluating our approach on the Munich dataset.
Table Performance metrics for each model variant on the Munich dataset.
Pairwise Acc.
NDCG@ 10
Cand. Diversity
Runtime/iter (ms)
Method
Simulation
Prior
PBO
LLM
Static
0.544 0.011
0.697 : 0.020
0.767 0.028
Statistical
Random
0.498 +: 0.013
0.491 = 0.043
0.989 : 0.045
0 146 + 6
Static
0.468 = 0.016
0.721 = 0.025
0.754 0.027
PBO AE
LLM
Direct Elicit
0.476 0.053
0.571 0.057
0.753 0.071
41447
Prob. Elicit
0.569 0.037
0.651
0.038
0,860 0.039
442 :: 21
Static
0.492 0.221
0.592 0.221
0.772 0.293
311 +
Statistical
Static
0.562 0.022
0.732 # 0.030
1.167 0.074
shows the results we obtained after evaluating our approach on the Idealista18 dataset using open-source LLM.
Open-source LLM
Table S
Table Performance metries for each model variant using the open-source -pt-oss-100 120b Ope- nAT et al. (2225) LLM on the Idealista/8 dataset.
Pairwise Acc.
NDCG@
Diversity
Runtime/iter (ms)
Method
Simulation
Prior
PBO
LLM
Static
0.504 + 0.019
0.610 = 0.033
0.596 0.050
1768 56
PBO AB
LLM
Direct Elicit
0.558 0.031
0.578 = 0.045
0.735 0.101
2121 153
Prob. Elicit
0.573 0.026
0,615 + 0.037
0.689 0.076
2109
Static
0.565 0.026
0.575 = 0.042
1.185 0.104
989 63
Figure shows a comparison between our proposed approach (PBO+AE with probabilistic LLM-based prior elicitation) and PBO+AE with the static prior and cold start.
Warm Start vs. Cold Start
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0.70
0.8
0.63
0.7
Pairwie Accuracy
0.60
NDCGGR0
0.55
0.6
0.50
0.5
0.45
0.40
Cald Start (LLM)
0.4
Cold Siari (LLMM)
(LLM)
(LLM)
10
20
30
20
Queries
Queries
(a) Warm vs. cold start on pairwise ranking accuracy.
(b) Warm vs. start on NDCG@
Figure 5: Comparison of our approach (PBO+AE with probabilistic LLM-based prior) and PBO+AE with the static prior and without warm start period in LLM-based simulation.
A.4 AE TRAINING
Table 9: AE hyperparameter configuration.
Parameter
Value
Batch Size
64
Dropout Rate
0.01
Hidden Dim 1
11
Hidden Dim 2
Latent Dim
6
Learning Rate
0.0026
LR Scheduler Factor
0.8
Min LR
10-6
Scheduler Patience
100
Num Epochs
250
Weight Decay
0.0013
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B ETHICS STATEMENT
Our proposed elicitation framework warrants consideration of several ethical dimensions, primarily concerning the use of LLMs for user simulation and the potential for unfairness in the real-estate application domain.
First, our reliance LLMs to generate personas for evaluation introduces risk of incorporat- ing and amplifying societal biases. LLMs are trained on vast corpora of text from the internet, which can contain stereotypical or prejudiced associations related demographics, socioeconomic status, and housing preferences. Consequently, the simulated personas may not represent diverse and au- thentic range of human decision-making, but instead reflect biased patterns. Optimizing our system against these simulated preferences could inadvertently lead to model that caters to stereotypes, rather than genuine user needs.
Second, the application of this framework to real estate recommendations could raise faimesss con- cerns, particularly regarding some features used in our dataset. Metrics such as the safety score and noise score often derived from data that can act as proxies for the racial socioeconomic composition of neighborhood. Using such features to guide recommendations risks perpetuating residential segregation by steering certain the additional need for caution when working with this type of data. The recommendations gener- ated by our system should not be interpreted as objective truths, but outputs of model trained on potentially biased data.
away from towards specific areas. We recognize
C REPRODUCIBILITY STATEMENT
The Munich dataset currently not publicly available due to licensing limitations.
The Idealista/8 18 dataset is publicly available httpss///gittuub..cmm/aaaaah///
ealistal8
The gpt-oss-120b model is available at tttpps://aagiinfacccccooooaalll
E-oss-120b
The Python implementation of all experiments is y
available a https::///gt)
ub htt.:p//...aaaaaaaaaa.aaaaaaaaaa
--thhellaeet- -latent-spaal space
D STATEMENT ON THE USE OF LLMS
LLMs have been used part of our methodology for prior generation (Sec. 3.2.1 and for user simulation (Sec. 4.2). In the creation of this manuscript, LLMs have been used for the initial literature search and editorial purposes.
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Reviews:
[{"review_id": "TYbDA2CAAo", "reviewer": "Reviewer_9kWw", "summary": "The paper proposes an interactive preference elicitation framework for complex, infrequent decisions like housing purchase choice. It combines LLMs (to generate personalized priors from natural language) and an autoencoder (to reduce feature dimensionality) within a PBO that learns user utility from pairwise comparisons in real time, evaluated on rental datasets from two European cities.", "questions": "- In line 64-67, what is the purpose/intuition of still using LLM for the continuous feature space? Even after reading Section 3.2.1, my question about this does not go away.\n- In line 82, you may want to elaborate/define the meaning of ``interactive preference elicitation'' which has appeared in the abstract & title but nowhere else before line 82 in the intro.\n- What is the purpose of the warm-start, and any more empirical results to show the importance of this?\n- In the experiment, what is the intuition for the vanilla (statistical) having an opposite trend (increase first and then decrease over the number of queries) with other benchmarks? \n\nI might raise my rating after seeing the rebuttal.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is well-written with clear structures. The problem itself is well motivated.", "weaknesses": "The intuition for the selection of each structure for each purpose could be strengthened. Please refer to the questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4qit3tanej", "reviewer": "Reviewer_EyRm", "summary": "The paper targets high-stakes, sparse-interaction decisions such as housing selection, where conventional recommender systems fail due to limited feedback and high-dimensional features. To address this, it proposes an interactive preference elicitation framework that combines Preferential Bayesian Optimization (PBO) with two key components: LLM-based probabilistic priors, which interpret natural-language interviews to initialize user utility weights and mitigate cold start; and an Autoencoder-based latent representation, which reduces dimensionality for efficient exploration. The system learns a latent utility model from pairwise user comparisons using a Gaussian-process surrogate and qEUBO acquisition for adaptive querying. Evaluations on real-estate datasets from Madrid and Munich show that latent-space PBO improves ranking accuracy and that LLM-guided priors significantly enhance sample efficiency and cold-start performance.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The paper tackles an important and realistic problem of learning preferences in high-stakes, sparse-feedback decision settings. It proposes a coherent framework that integrates LLM-based prior elicitation, Autoencoder latent-space learning, and Preferential Bayesian Optimization into a unified probabilistic approach. The method is well-founded, combining Gaussian Process modeling with qEUBO-based active querying for efficient preference learning. Experiments on real-estate datasets show clear improvements in ranking accuracy and cold-start performance, demonstrating both methodological novelty and strong practical relevance.", "weaknesses": "## Weaknesses\nA central modeling limitation lies in the treatment of noise within the preference likelihood. The paper explicitly assumes that both the user’s preference inconsistency and the autoencoder (AE) reconstruction error can be modeled as *jointly Gaussian and additive*. In Appendix A.1 (lines 593 page 11), the authors state that the decoder output can be written as $ \\hat{x} = h_\\theta(g_\\theta(x)) = x + \\epsilon $ with $ \\epsilon \\sim \\mathcal{N}(0, \\Sigma_\\epsilon) $, and that the user’s utility varies locally linearly, $ u(\\hat{x}) \\approx u(x) + \\nabla u(x)^\\top \\epsilon $. Combining these yields a single Gaussian error term $ \\eta $ with variance $ \\sigma^2 = \\sigma_{\\text{pref}}^2 + \\sigma_{\\text{recon}}^2 $ in the Probit likelihood, expressed as  \n$ P(z \\succ z') = \\Phi\\!\\left(\\frac{u(x) - u(x')}{\\sigma}\\right) $,  \nwhere $ \\sigma $ captures both preference noise and AE uncertainty (page 5).  \nWhile elegant, this simplification conflates heterogeneous uncertainty sources—human inconsistency, model reconstruction bias, and latent-space distortions—into one scalar variance term. It presumes homoscedastic, isotropic noise and local linearity of the utility function, which are rarely valid in complex, nonlinear real-estate feature spaces. In practice, AE errors vary significantly across regions of the feature manifold, and user responses exhibit contextual, multimodal variability. This global Gaussian assumption therefore risks *underestimating epistemic uncertainty*, leading to overconfident posterior estimates under the Laplace approximation and potentially premature exploitation in qEUBO selection.  \n\nBeyond these statistical concerns, the model’s abstraction of user feedback neglects potential heteroscedastic and structured noise patterns. Real users exhibit region-dependent reliability—for example, being consistent on low-price listings but noisy on high-price ones—yet the framework treats all feedback as equally noisy.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fthA1OftbF", "reviewer": "Reviewer_tdZs", "summary": "The authors propose an interactive preference elicitation (PE) framework based on preferential Bayesian optimization (PBO).\n\nIt learns a latent utility function for the user from pairwise comparisons queried to that user.\n\nTo address the cold start issue and obtain informative probabilistic priors of feature weights for the utility function, they use an LLM-guided user interview, instead of a predefined static weight vector.\n\nThey also use an autoencoder (AE) to obtain a latent representation of lower dimension that can be used improve the sample efficiency of exploration.\n\nThey evaluate their method on rental market datasets from two European cities (Madrid, Spain and Munich, Germany).", "questions": "Page 4: \"we do not explicitly denote data normalization\" What does this mean?\n\nHave you experimented with other LLMs, besides Gemini 2.5 Flash-Lite? Why not experiment with an open-source LLM? It would be good to run the experiments with other LLMs, to see how much the choice impacts performance.\n\nHow did you select the personas?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "This paper is well-motivated and addresses a real-world problem. As the authors state in the conclusion, it has immediate applications for online real estate platforms, where it could reduce user fatigue by minimizing the number of property comparisons needed to identify suitable options.\n\nThe presentation of the paper is mostly clean and easy to follow. Notation is properly introduced, and the order in which concepts are introduced is good.\n\nThe experimental results are detailed, including a high number of runs, graphs with confidence intervals, and a table with numerical metrics.", "weaknesses": "Experiments are limited to rental markets in two cities: Madrid, Spain and Munich, Germany.\n\nIt would be nice to see experiments beyond the rental market setting, to get a better assessment of how well the method performs in other domains.\n\nThe LLM that the authors used, Gemini 2.5 Flash-Lite, is closed-source.\n\nThe Munich dataset that the authors used is not publicly available due to licensing issues.\n\nMinor corrections:\n\nPage 3: Replace r << d with r \\ll d.\n\nPage 6: Replace \"as activation function\" with \"as the activation function\".\n\nSuggestions:\n\nPage 7: Since there's space, include the formula for NDCG@k.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)