10 citations · 15 across the 6 of their papers we have counts for
6 papers
Designing Trustworthy LLM-based Wellbeing Recommendation through Controllable Interaction
Alan Said, Alexandra Weilenmann
Large language models (LLMs) are increasingly used to generate personalized guidance in wellbeing contexts such as physical activity, stress management, and mental health support,…
Trust as a Situated User State in Social LLM-Based Chatbots: A Longitudinal Study of Snapchat's My AI
Annie Landerberg, Kari Flatmo, Alan Said
Social chatbots based on large language models are increasingly embedded in everyday platforms, yet how users develop trust in these systems over time remains unclear. We present a…
The Hidden Cost of Defaults in Recommender System Evaluation
Hannah Berling, Robin Svahn, Alan Said
Hyperparameter optimization is critical for improving the performance of recommender systems, yet its implementation is often treated as a neutral or secondary concern. In this wor…
Soundtracks of Our Lives: How Age Influences Musical Preferences
Arsen Matej Golubovikj, Bruce Ferwerda, Alan Said +1
The majority of research in recommender systems, be it algorithmic improvements, context-awareness, explainability, or other areas, evaluates these systems on datasets that capture…
Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations
Juan Ahmad, Jonas Hellgren, Alan Said
Recommender systems play a vital role in helping users discover content in streaming services, but their effectiveness depends on users understanding why items are recommended. In…
Understanding Fairness in Recommender Systems: A Healthcare Perspective
Veronica Kecki, Alan Said
Fairness in AI-driven decision-making systems has become a critical concern, especially when these systems directly affect human lives. This paper explores the public's comprehensi…