相关论文: Personalization Paradox in Behavior Change Apps: L…
The fast integration of artificial intelligence into mobile applications has completely changed the digital landscape; however, the impact of this change on user perception of AI features remains poorly understood. This large-scale analysis…
We address the personalized policy learning problem using longitudinal mobile health application usage data. Personalized policy represents a paradigm shift from developing a single policy that may prescribe personalized decisions by…
Personalized gamification explores knowledge about the users to tailor gamification designs to improve one-size-fits-all gamification. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity…
Machine Learning algorithms are technological key enablers for artificial intelligence (AI). Due to the inherent complexity, these learning algorithms represent black boxes and are difficult to comprehend, therefore influencing compliance…
Cooperation on social networks is crucial for understanding human survival and development. Although network structure has been found to significantly influence cooperation, human experiments have observed different cooperation phenomena…
Physical activity is known to help improve and maintain one's health. In particular, recreational running has become increasingly popular in recent years. Yet, lack of motivation often interferes with people's routines and thus may prohibit…
In the past few years, Artificial Intelligence (AI) has garnered attention from various industries including financial services (FS). AI has made a positive impact in financial services by enhancing productivity and improving risk…
Despite rapid technological progress, effective human-machine cooperation remains a significant challenge. Humans tend to cooperate less with machines than with fellow humans, a phenomenon known as the machine penalty. Here, we show that…
As LLMs become capable of complex tasks, there is growing potential for personalized interactions tailored to the subtle and idiosyncratic preferences of the user. We present a public benchmark, PersonalLLM, focusing on adapting LLMs to…
Various modern and highly popular applications make use of user data traces in order to offer specific services, often for the purpose of improving the user's experience while using such applications. However, even when user data is…
As robots and digital assistants are deployed in the real world, these agents must be able to communicate their decision-making criteria to build trust, improve human-robot teaming, and enable collaboration. While the field of explainable…
With the growing utility of today's conversational virtual assistants, the importance of user motivation in human-AI interaction is becoming more obvious. However, previous studies in this and related fields, such as human-computer…
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with…
This paper sets out a process of app analysis intended to support understanding of use but also redesign. From usage logs we infer activity patterns - Markov models - and employ probabilistic formal analysis to ask questions about the use…
User interface personalization enhances digital efficiency, usability, and accessibility. However, in user-driven setups, limited support for identifying and evaluating worthwhile opportunities often leads to underuse. We explore a…
People frequently face challenging decision-making problems in which outcomes are uncertain or unknown. Artificial intelligence (AI) algorithms exist that can outperform humans at learning such tasks. Thus, there is an opportunity for AI…
Many projects (both practical and academic) have designed algorithms to match users to content they will enjoy under the assumption that user's preferences and opinions do not change with the content they see. Evidence suggests that…
To effect behavior change a successful algorithm must make high-quality decisions in real-time. For example, a mobile health (mHealth) application designed to increase physical activity must make contextually relevant suggestions to…
As artificial intelligence transforms software development, a critical question emerges: how can developers and AI systems collaborate most effectively? This dissertation optimizes human-AI programming roles through self-determination…
This thesis investigates the psychological factors that influence belief in AI predictions, comparing them to belief in astrology- and personality-based predictions, and examines the "personal validation effect" in the context of AI,…