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Found 5 papers, 1 papers with code
Large Language Models as Conversational Movie Recommenders: A User Study
This paper explores the effectiveness of using large language models (LLMs) for personalized movie recommendations from users' perspectives in an online field experiment.
What Are We Optimizing For? A Human-centric Evaluation Of Deep Learning-based Recommender Systems
Deep learning-based (DL) models in recommender systems (RecSys) have gained significant recognition for their remarkable accuracy in predicting user preferences.
Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems
In this study, we partition users by rating disposition - looking first at their percentage of negative ratings, and then at the general use of the rating scale.
Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models
A single digital newsletter usually contains many messages (regions).
We Are in This Together: Quantifying Community Subjective Wellbeing and Resilience
Notably, we find that communities that talked more about social ties normally experienced in-person, such as friends, family, and affiliations, were actually more likely to be impacted.
