Adaptive preference measurement with unstructured data
R Dew - Management Science, 2024 - pubsonline.informs.org
Many products are most meaningfully described using unstructured data such as text or
images. Unstructured data are also common in e-commerce, in which products are often …
images. Unstructured data are also common in e-commerce, in which products are often …
Probabilistic machine learning: New frontiers for modeling consumers and their choices
Making sense of massive, individual-level data is challenging: marketing researchers and
analysts need flexible models that can accommodate rich patterns of heterogeneity and …
analysts need flexible models that can accommodate rich patterns of heterogeneity and …
A theory-based explainable deep learning architecture for music emotion
This paper develops a theory-based, explainable deep learning convolutional neural
network (CNN) classifier to predict the time-varying emotional response to music. We design …
network (CNN) classifier to predict the time-varying emotional response to music. We design …
A theory-based interpretable deep learning architecture for music emotion
This paper develops a theory-based, explainable deep learning convolutional neural
network (CNN) classifier to predict the time-varying emotional response to music. We design …
network (CNN) classifier to predict the time-varying emotional response to music. We design …
Using quantum game theory to model competition
VR Rao, N Yang, S Zakerinia - Cornell SC Johnson College of …, 2024 - papers.ssrn.com
This paper examines the potential of quantum game theory in rethinking strategic
decisionmaking across foundational economic models. By embedding principles from …
decisionmaking across foundational economic models. By embedding principles from …
Demand estimation with text and image data
We propose a demand estimation method that allows researchers to estimate substitution
patterns from unstructured image and text data. We first employ a series of machine learning …
patterns from unstructured image and text data. We first employ a series of machine learning …
Unraveling multifaceted user preferences on content platforms: A Bayesian deep learning approach
M Yin, Z Cong, J Liu - Available at SSRN 5051742, 2024 - papers.ssrn.com
Given the increasing importance of user engagement on digital content platforms, this paper
proposes a Bayesian deep learning model, called the Multi-Dynamic Neural Poisson …
proposes a Bayesian deep learning model, called the Multi-Dynamic Neural Poisson …
Learning Design Preferences through Design Feature Extraction and Weighted Ensemble
D Shin, S Lee, N Kang - arxiv preprint arxiv:2405.07193, 2024 - arxiv.org
Design is a factor that plays an important role in consumer purchase decisions. As the need
for understanding and predicting various preferences for each customer increases along …
for understanding and predicting various preferences for each customer increases along …
Using AI for controllable stimuli generation: An application to gender discrimination with faces
Natural stimuli involving unstructured data like images can be decomposed into many latent
features, which may be highly correlated and partially unobserved to the researcher. This …
features, which may be highly correlated and partially unobserved to the researcher. This …
Beyond Fake or Genuine--The Effect of Large Language Models (LLMs) on the Content and Sentiment of Product Reviews
Abstract “ChatGPT has revolutionized the way people engage with and create product
reviews... Its ability to understand and generate human-like text enables it to generate high …
reviews... Its ability to understand and generate human-like text enables it to generate high …