Convergence of Chatbot Personalities Using Reinforcement Learning and Text Generation

Chatbots have become indispensable tools in customer support and mental health industries, facilitating efficient communication and problem-solving. However, the success of these interactions often hinges on the alignment between the chatbot's personality and the user's preferences. This study investigates the significance of personality matching in chatbot conversations, exploring the impact of MBTI personality types on user engagement and satisfaction. We delve into various strategies for adopting appropriate tone and persona in one-to-one and group chatbot interactions. By understanding the nuances of personality and tailoring chatbot responses accordingly, we aim to optimize the effectiveness of these virtual agents and enhance the overall user experience.

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Publication Details

Journal
Proceedings of the AAAI Symposium Series
Published
2025-05-28
DOI
https://doi.org/10.1609/aaaiss.v5i1.35608
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
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article

Convergence of Chatbot Personalities Using Reinforcement Learning and Text Generation

Rajib Kar, Partha Kar
Proceedings of the AAAI Symposium Series
Topic Modeling
article

Convergence of Chatbot Personalities Using Reinforcement Learning and Text Generation

Rajib Kar, Partha Kar
article en

Abstract

Chatbots have become indispensable tools in customer support and mental health industries, facilitating efficient communication and problem-solving. However, the success of these interactions often hinges on the alignment between the chatbot's personality and the user's preferences. This study investigates the significance of personality matching in chatbot conversations, exploring the impact of MBTI personality types on user engagement and satisfaction. We delve into various strategies for adopting appropriate tone and persona in one-to-one and group chatbot interactions. By understanding the nuances of personality and tailoring chatbot responses accordingly, we aim to optimize the effectiveness of these virtual agents and enhance the overall user experience.

Proceedings of the AAAI Symposium SeriesVol. 5(1)
University of California System (US)
Openalex Percentile: Top 94%
Topic Modeling
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