Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome

arXiv:2404.13792v1 Announce Type: cross
Abstract: Customizing persuasive conversations related to the outcome of interest for specific users achieves better persuasion results. However, existing persuasive conversation systems rely on persuasive strategies and encounter challenges in dynamically adjusting dialogues to suit the evolving states of individual users during interactions. This limitation restricts the system's ability to deliver flexible or dynamic conversations and achieve suboptimal persuasion outcomes. In this paper, we present a novel approach that tracks a user's latent personality dimensions (LPDs) …

abstract adjusting arxiv challenges conversation conversations counterfactual cs.ai cs.cl cs.hc cs.mm dimensions however interactions personality persuasion reasoning results strategies systems type