HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning
This addresses the issue of impersonal interactions in conversational AI for users, though it appears incremental as it builds on existing methods like reinforcement learning and user profiling.
The paper tackled the problem of generic conversational AI by introducing HumAIne-chatbot, which personalizes responses using a user profiling framework and reinforcement learning, resulting in significant improvements in user satisfaction, personalization accuracy, and task achievement in experiments with 50 synthetic personas.
Current conversational AI systems often provide generic, one-size-fits-all interactions that overlook individual user characteristics and lack adaptive dialogue management. To address this gap, we introduce \textbf{HumAIne-chatbot}, an AI-driven conversational agent that personalizes responses through a novel user profiling framework. The system is pre-trained on a diverse set of GPT-generated virtual personas to establish a broad prior over user types. During live interactions, an online reinforcement learning agent refines per-user models by combining implicit signals (e.g. typing speed, sentiment, engagement duration) with explicit feedback (e.g., likes and dislikes). This profile dynamically informs the chatbot dialogue policy, enabling real-time adaptation of both content and style. To evaluate the system, we performed controlled experiments with 50 synthetic personas in multiple conversation domains. The results showed consistent improvements in user satisfaction, personalization accuracy, and task achievement when personalization features were enabled. Statistical analysis confirmed significant differences between personalized and nonpersonalized conditions, with large effect sizes across key metrics. These findings highlight the effectiveness of AI-driven user profiling and provide a strong foundation for future real-world validation.