8 citations · 16 across the 8 of their papers we have counts for
8 papers
SteerEval: A Framework for Evaluating Steerability with Natural Language Profiles for Recommendation
Joyce Zhou, Weijie Zhou, Doug Turnbull +1
Natural-language user profiles have recently attracted attention not only for improved interpretability, but also for their potential to make recommender systems more steerable. By…
End-to-end Training for Recommendation with Language-based User Profiles
Zhaolin Gao, Joyce Zhou, Yijia Dai +1
There is a growing interest in natural language-based user profiles for recommender systems, which aims to enhance transparency and scrutability compared with embedding-based metho…
Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability
Jinsook Lee, Emma Harvey, Joyce Zhou +3
Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-20…
Language-Based User Profiles for Recommendation
Joyce Zhou, Yijia Dai, Thorsten Joachims
Most conventional recommendation methods (e.g., matrix factorization) represent user profiles as high-dimensional vectors. Unfortunately, these vectors lack interpretability and st…
GPT as a Baseline for Recommendation Explanation Texts
Joyce Zhou, Thorsten Joachims
In this work, we establish a baseline potential for how modern model-generated text explanations of movie recommendations may help users, and explore what different components of t…
Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters
Jinsook Lee, Bradon Thymes, Joyce Zhou +2
University admission at many highly selective institutions uses a holistic review process, where all aspects of the application, including protected attributes (e.g., race, gender)…
An Interactive UI to Support Sensemaking over Collections of Parallel Texts
Joyce Zhou, Elena Glassman, Daniel S. Weld
Scientists and science journalists, among others, often need to make sense of a large number of papers and how they compare with each other in scope, focus, findings, or any other…
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal, Tongshuang Wu, Joyce Zhou +5
Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, pr…