12 citations · 14 across the 9 of their papers we have counts for
10 papers
Learning Heterogeneous Preferences
Shiwali Mohan, Matt Hong, Dule Shu +3
Learning from human feedback has become a central paradigm for training modern AI systems, where models of human utility are used as reward models in policy learning. Existing meth…
Designing Rewards for Rewarding Designs: Demonstrating the Impact of Rewards on the Creative Design Process
Surabhi S Nath, Vindula Jayawardana, Monica Van +2
The creative design process involves transforming abstract goals into concrete outcomes through a series of decisions made under constraints. While such processes are commonly shap…
Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs
Taewook Kim, Matthew K. Hong, Yan-Ying Chen +5
Product designers often begin their design process with handcrafted personas. While personas are intended to ground design decisions in consumer preferences, they often fall short…
Deconstructing Taste: Toward a Human-Centered AI Framework for Modeling Consumer Aesthetic Perceptions
Matthew K. Hong, Joey Li, Alexandre Filipowicz +6
Understanding and modeling consumers' stylistic taste such as "sporty" is crucial for creating designs that truly connect with target audiences. However, capturing taste during the…
Learning to Represent Individual Differences for Choice Decision Making
Yan-Ying Chen, Yue Weng, Alexandre Filipowicz +7
Human decision making can be challenging to predict because decisions are affected by a number of complex factors. Adding to this complexity, decision-making processes can differ c…
ConjointNet: Enhancing Conjoint Analysis for Preference Prediction with Representation Learning
Yanxia Zhang, Francine Chen, Shabnam Hakimi +9
Understanding consumer preferences is essential to product design and predicting market response to these new products. Choice-based conjoint analysis is widely used to model user…
Understanding the Cognitive Complexity in Language Elicited by Product Images
Yan-Ying Chen, Shabnam Hakimi, Monica Van +4
Product images (e.g., a phone) can be used to elicit a diverse set of consumer-reported features expressed through language, including surface-level perceptual attributes (e.g., "w…
Personalizing Driver Safety Interfaces via Driver Cognitive Factors Inference
Emily S Sumner, Jonathan DeCastro, Jean Costa +14
Recent advances in AI and intelligent vehicle technology hold promise to revolutionize mobility and transportation, in the form of advanced driving assistance (ADAS) interfaces. Al…
Visual Elements and Cognitive Biases Influence Interpretations of Trends in Scatter Plots
Alexandre Filipowicz, Scott Carter, Nayeli Bravo +6
Visualizations are common methods to convey information but also increasingly used to spread misinformation. It is therefore important to understand the factors people use to inter…
Generative AI for Product Design: Getting the Right Design and the Design Right
Matthew K. Hong, Shabnam Hakimi, Yan-Ying Chen +3
Generative AI (GenAI) models excel in their ability to recognize patterns in existing data and generate new and unexpected content. Recent advances have motivated applications of G…