Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction

ArXi:2601.05654v3 Announce Type: replace Estimating the persuasiveness of messages is critical in various applications, from recommender systems to safety assessment of LLMs. While it is imperative to consider the target persuadee's characteristics, such as their values, experiences, and reasoning styles, there is currently no established systematic framework to optimize leveraging a persuadee's past activities (e.g., conversations) to the benefit of a persuasiveness prediction model.