Study uses EEG and ML to predict movie ratings with 72% accuracy.
problem Predicting consumer preferences for movie trailers.
method EEG and machine learning techniques to analyze brain responses to movie trailers.
result Predicted movie ratings with 72% accuracy.
Bayesian algorithm learns consumer preferences for energy-saving home automation.
problem Effective energy saving for residential consumers in real-time tariffs.
method Bayesian learning algorithm to estimate comfort level from appliance use history.
result Algorithm outperforms regression analysis in numeric experiments with simulated consumer behavior.
GBS uses machine learning to design products based on consumer preferences.
problem Designing products to meet consumer preferences.
method GBS is a discrete choice experiment that uses machine learning to adaptively construct paired comparison questions.
result GBS outperforms existing methods in accuracy and sample efficiency.
The paper sorts big data by revealed preferences, improving consumer and policy decisions.
problem Sorting diverse consumer preferences for big data objects like colleges.
method Endogenous weighting of revealed preferences, considering spillover effects.
result Consistent steady-state solution to counterbalance equilibrium.
The paper improves consumer preference modeling by considering multiple product categories.
problem Estimating consumer preferences across multiple product categories with varying attributes and price sensitivity.
method Extends matrix factorization techniques to account for time-varying product attributes and out-of-stock products, pooling information across categories to estimate heterogeneity in preferences.
result The model improves over traditional approaches, accurately estimating consumer preferences and price sensitivity.
The paper analyzes consumer preferences for restaurants using mobile location data.
problem Understanding and predicting consumer choices for restaurants based on location data.
method Bayesian approach with variational inference and stochastic gradient descent.
result The model outperforms standard models in predicting consumer behavior.
Paper proposes a method to estimate consumer valuations from bundle sales data.
problem Estimating consumer valuations from bundle sales data using classical methods is challenging.
method Proposes an approach using EM algorithm and Monte Carlo simulation to estimate consumer valuations from bundle sales data.
result The approach can recover the distribution of consumers' valuations and is robust to unobserved no-purchases and clustered market segments.
The paper develops a method to estimate consumer preferences from observed rankings.
problem Estimating consumer preferences from partial ranking information.
method Interpreting observed rankings as pairwise comparisons, modeling latent utility, and correcting for selection bias.
result The method improves recommendation performance, especially for previously unconsumed products.
IRL approach for studying consumer demand from observed behavior.
problem Confusing observational noise with consumer heterogeneity.
method Developed a Maximum Entropy IRL model for low-dimensional convex optimization.
result Observational noise can be mistaken for consumer heterogeneity.
New adaptive questionnaire identifies optimal product design without precise preference estimates.
problem Identifying the most profitable product design from unknown consumer preferences.
method Integrates engineering feasibility and cost models with adaptive discrete-choice questionnaire to directly determine the optimal design.
result The optimal design can be determined without accurate preference estimation, leveraging engineering knowledge.
Proposes a new method for optimizing designs based on uncertain preferences.
problem Optimizing designs with uncertain and time-consuming preferences.
method Bayesian optimization with preference learning for multi-attribute optimization.
result Produces a menu of designs and attributes for the DM to choose from.
New study shows personalized content recommendations can lead to polarization of user preferences.
problem Personalized content recommendations can alter user preferences, leading to polarization.
method Used a model of preference dynamics to explore how personalized content affects user preferences.
result Standard reward maximization algorithms achieve only constant regret in personalized recommendation environments.
Consumers adjust their spending based on firms' social stances, influencing firm profits.
problem How consumer spending responds to firms' social stances.
method Using payment card transactions to predict and measure consumer responses to firms' social stances.
result Consumers' spending increases by 19% and decreases by 12% in response to firms' social stances, with effects lasting up to a year.
Active learning recovers choice model from noisy data.
problem Identifying non-parametric choice models from noisy data.
method Directed acyclic graph (DAG) representation and inclusion-exclusion approach.
result Algorithm more accurately recovers frequent preferences.
Study on self-consuming generative models with diverse human curation, focusing on convergence and stability.
problem Analyzing self-consuming generative models with heterogeneous human curation.
method Investigates the asymptotic behavior of retraining dynamics using nonlinear Perron--Frobenius theory and Banach contraction mapping.
result Improves convergence results and provides stability and non-stability analyses for the model.
Analyzes new economic paradigm for non-independent consumer choices.
problem Non-independent consumer choices due to firm supply and consumer information.
method Develops a new mathematical framework for economic systems.
result New paradigm for economic system description is necessary.
Proposes a model to optimize feedback for content creators on social media.
problem Nurturing content creation on social media platforms.
method Modeling feedback distribution to incentivize creators and optimize newsfeed experience.
result Optimized newsfeed for content creators without compromising user experience.
Enhanced word embedding creates new consumer-friendly health terms.
problem Laymen's health terms are often jargon and hard to understand.
method Developed an enhanced GloVe word embedding technique to generate new consumer-friendly terms.
result New CHV terms generated from consumer-generated text.
The algebra of transactions as fundamental measurements is constructed on the basis of the analysis of their properties and represents an expansion of the Boolean algebra. The notion of the generalized economic measurements of the economic quantity and quality of objects of transactions is introduced. It has been shown…
This paper analyzes popular time-nonseparable utility functions that describe "habit formation" consumer preferences comparing current consumption with the time averaged past consumption of the same individual and "catching up with the Joneses" (CuJ) models comparing individual consumption with a cross-sectional averag…
The paper shows how curated synthetic data can optimize human preferences in generative models.
problem Contamination of web-scale datasets by synthetic data affects future model training.
method Theoretical study of iterated retraining of generative models with curated synthetic data.
result Data curation can be seen as an implicit preference optimization mechanism, maximizing expected reward.
NESA learns user preferences and calendar contexts for efficient event scheduling.
problem Challenges in understanding user preferences and complex calendar contexts for automated event scheduling.
method Leverages deep neural networks to learn user preferences and calendar context from raw online calendars.
result Significantly outperforms previous models in personal and multi-attendee event scheduling tasks.
Optimal investment strategy for a large, identical investor or pension fund.
problem Finding the best investment strategy for a large group of identical investors.
method Developed a numerical algorithm and derived an analytic formula for optimal consumption.
result Proved the model's validity for both large and small groups of investors.
Develops a statistical framework to measure uncertainty in model rankings based on human preferences.
problem Uncertainty in model rankings based on human preferences due to mismatch between human and model preferences.
method Statistical framework using pairwise comparisons by humans and models to provide rank-sets for each model.
result Rank-sets constructed using only pairwise comparisons by strong models often do not cover the true ranking of human preferences.
Tax effects on consumer behavior are ambiguous due to irrationality and limited willpower.
problem Ambiguity in tax effects on consumer behavior due to irrationality and limited willpower.
method Examined through behavioral and neuroeconomics, analyzing consumer behavior in real life.
result Tax effects on consumer behavior are ambiguous due to irrationality and limited willpower.
Proposes a new framework for resource-limited recommendation.
problem Resource constraints affect user choices in recommendation tasks.
method Interest-behavior multiplicative network with MRRNNs and resource-limited branch.
result Framework effectively predicts user interactions considering resource limitations.
Network analysis reveals hidden user preferences for platform steering.
problem Limited understanding of user preferences in data gatekeepers.
method Network science and a new measure called Network Information Patrimony.
result Platforms can use network data to better estimate user preferences.
Aesthetic-based clothing recommendation improves user satisfaction.
problem Lack of aesthetic features in existing clothing recommendation methods.
method Introduce aesthetic features extracted by a neural network and incorporate them into a personalized tensor factorization model.
result Our approach significantly outperforms state-of-the-art recommendation methods.
Improved ranking method for scarce data with feature info.
problem Ranking items with limited comparisons and feature data.
method Modified RankCentrality using diffusion methods for feature info.
result Meaningful rankings even with scarce comparisons.
Model proposes how regulators should oversee complex algorithms in high-stakes applications.
problem Regulating complex algorithms used in high-stakes applications like lending, testing, and hiring.
method Proposes a model where regulators are limited in learning about complex algorithms with misaligned preferences, and explores different regulatory approaches.
result Complex algorithms can improve welfare, but regulation should focus on the source of incentive misalignment for optimal results.
Researchers analyze optimal investment strategies for a collectivised pension fund with identical investors.
problem Optimizing investment strategies for a collectivised pension fund with identical investors.
method Analytical computation of optimal investment-consumption strategies for a fund of n identical investors with Epstein-Zin preferences.
result Constant consumption strategy is suboptimal for infinite collectives, suggesting annuities and defined benefit investments are suboptimal.
Investment and consumption strategies with luxury goods for retirement age.
problem Optimal investment and consumption with heterogeneous goods and retirement timing.
method PDE and stochastic control theory, variational inequality, dual transformation.
result Optimal consumption strategies and retirement policies for utility maximizers.
Bayesian model estimates feature values of premium products.
problem Understanding the monetary value of product features.
method Bayesian Hierarchical Logit Model
result Successfully recovers true feature valuations from data.
A nonparametric pricing policy learns customer preferences from covariates without prior assumptions.
problem Lack of historical data limits personalized pricing for new businesses.
method Nonparametric pricing policy that clusters customers based on covariates and preferences.
result Regret of order O(log(T)2T(2+d)/(4+d)) for a finite horizon T. Study copyright's impact on creative industries using AI-generated fonts.
problem Estimating supply and demand in creative industries with AI-generated content.
method Neural network embeddings, spatial regression, event-study analyses, structural model of supply and demand.
result Copyright can raise consumer welfare by encouraging product relocation.
Paper proposes a data augmentation method for LLM-generated data in market research.
problem Bias in LLM-generated data in market research.
method Statistical data augmentation approach integrating LLM-generated and real data.
result Statistically robust estimators with reduced bias and cost savings.
A new auction model for zero-marginal cost products.
problem Determining fair prices for zero-marginal cost products.
method Continuous bidding with self-organized criticality mechanism.
result Avalanches of sales above a critical price.
Research examines motivations and factors influencing retailers' payment method choices.
problem Understanding motivations and factors affecting retailers' payment method choices.
method Qualitative and quantitative analysis of various factors including regulatory constraints, merchant service providers, and demographic variables.
result Lower interchange fees and regulatory constraints make card payment adoption financially feasible for merchants.
We study consumption behaviour in systems with heterogeneous interacting agents. Two different models are introduced, respectively with long and short range interactions among agents. At any time step an agent decides whether or not to consume a good, doing so if this provides positive utility. Utility is affected by i…
A fast method estimates stability of ensemble feature selectors.
problem Improving stability of ensemble feature selectors for better prediction.
method Simulator of a feature selector to estimate stability.
result Reduces computation time for estimating stability.
Investors adjust spending based on a social norm, spending less during losses and more during gains.
problem Managing spending and portfolio decisions while adhering to a social norm.
method Formulated a preference ordering with two CRRA preference orderings, solved analytically and numerically.
result Annual spending should be lower than expected financial return and procyclical, with spending cuts following losses.
New method improves reliability of recommender systems in face of fake accounts and manipulation.
problem Improving reliability of recommender systems in the presence of fake accounts and manipulation.
method Robust Discrete Matrix Completion (RDMC) method designed to handle sparse rating data and manipulation.
result Evaluations show RDMC offers a statistically-sound blueprint for future studies on recommender systems.
LDA identifies latent topics in CFPB consumer complaints over time.
problem Identify latent topics in CFPB consumer complaints for better regulation effectiveness.
method Latent Dirichlet Allocation (LDA) for topic modeling of consumer complaints.
result Time trends of latent topics reveal regulatory effectiveness and consumer protection issues.
This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to …
We extend the lifecycle model (LCM) of consumption over a random horizon (a.k.a. the Yaari model) to a world in which (i.) the force of mortality obeys a diffusion process as opposed to being deterministic, and (ii.) a consumer can adapt their consumption strategy to new information about their mortality rate (a.k.a. h…
Paper presents a shape-based approach for better household load curve clustering and prediction.
problem Difficulty in classifying and predicting consumer energy consumption due to many clusters.
method Shape-based approach using Dynamic Time Warping (DTW) to align energy consumption patterns.
result Reduces the number of representative groups by 50% and improves prediction accuracy.
This study examines how fashion consumption affects self-confidence and buying behavior in Iranian consumers.
problem Understanding the role of self-confidence in fashion buying behavior.
method A questionnaire was used to collect data from 400 consumers in Tehran's clothing market. Structural equations and factor analysis were employed to test the model.
result Interest in fashion, personal taste, utilitarianism, and new products positively impact self-confidence, and self-confidence positively impacts fashion buying behavior.
Method detects multi-timescale consumer spending patterns from receipts.
problem Understanding and managing consumer behavior in high-dimensional data.
method Non-negative tensor factorization (NTF) to extract multi-timescale expenditure patterns.
result Consumption patterns are characterized based on spending behavior over different timescales.