📄 Research Paper Abstract
Below is the abstract from this arXiv research paper. Mathematical notation has been simplified for readability.
Modeling user preferences across domains remains a key challenge in slate recommendation (i.e. recommending an ordered sequence of items) research. We investigate how Large Language Models (LLM) can effectively act as world models of user preferences through pairwise reasoning over slates. We conduct an empirical study involving several LLMs on three tasks spanning different datasets. Our results reveal relationships between task performance and properties of the preference function captured by LLMs, hinting towards areas for improvement and highlighting the potential of LLMs as world models in recommender systems.
Read Full Paper on arXiv
View the complete research paper with proper mathematical formatting, figures, and references on arXiv.