LLMs and Recommenders

My four-year PhD program embarks on an insightful journey to explore the influence of foundational models, particularly Large Language Models (LLMs), on recommender interfaces. This research aims to bridge the gap between cutting-edge technology and user-centric design, focusing on how AI can enhance the user experience in recommender systems, thereby contributing to the rapidly evolving field of intelligent user interfaces,

The first phase of my research delves into the transformative role of LLMs in shaping more interactive and engaging recommender user experiences. Here, I map out how these advanced models can redefine user interaction, making it more intuitive and appealing. Central to this phase is not only the exploration of the potential of LLMs but also a critical examination of the risks involved. Ensuring the ethical application of these technologies in real-world scenarios is a cornerstone of my study, as it balances innovation with responsibility.

Shifting gears in the second phase, the research will pivot towards the creation and evaluation of user interfaces. This phase is dedicated to experimenting with novel layout and presentation patterns, rigorously assessing them both quantitatively and qualitatively. The goal is to uncover design strategies that align with the diverse objectives of various stakeholders in the recommender system domain.

By intertwining state-of-the-art AI research with the underexplored realm of user interaction in recommender systems, my project aims to make significant contributions to the discourse on ethical AI applications. The insights gained are expected to fuel discussions around AI ethics in recommender systems and inspire professionals to adopt more user-friendly and effective design patterns. Ultimately, this research seeks to set a new benchmark in how we integrate generative AI technologies into user interfaces, ensuring they are not only advanced but also ethical and user-centric.