Open Access

ARTICLE

Design of Artificial Intelligence Companion Chatbot

by Xiaoying Chen^1,*, Jie Kang¹, Cong Hu²

1 College of Mechanical & Electrical Engineering, Sanjiang University, Nanjing, 210012, China
2 Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin, 541004, China

* Corresponding Author: Xiaoying Chen. Email:

Journal of New Media 2024, 6, 1-16. https://doi.org/10.32604/jnm.2024.045833

Received 08 September 2023; Accepted 08 February 2024; Issue published 28 March 2024

Abstract

With the development of cities and the prevalence of networks, interpersonal relationships have become increasingly distant. When people crave communication, they hope to find someone to confide in. With the rapid advancement of deep learning and big data technologies, an enabling environment has been established for the development of intelligent chatbot systems. By effectively combining cutting-edge technologies with human-centered design principles, chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness. A multi-topic chat companion robot based on a state machine has been proposed, which can engage in fluent dialogue with humans and meet different functional requirements. It can chat with users about movies, music, and other related topics, and recommend movies and music that may interest them to alleviate their loneliness and provide companionship. The interaction platform of the companion robot is realized through the QQ communication platform, with two chat modes: Conversation mode and recommendation mode. First, the KdConv open-source corpus was selected, and Python was used to crawl information on movies and music from Douban and QQ Music to establish and pre-process the dataset. Then, the dialogue function was implemented using generative language models and retrieval systems, while the recommendation function was achieved using user profiling and collaborative filtering. Finally, a state machine algorithm was used to achieve real-time switching between the two chat modes of the companion robot. In conclusion, test participants gave high ratings for the accuracy of the companion robot’s responses and the satisfaction with its content recommendations. Compared to traditional large-scale integrated models, this robot employs a state-machine framework to achieve diverse functions through seamless state transitions, thereby enhancing computational speed and precision. Additionally, the robot can recommend movies and music, providing companionship and alleviating loneliness for users, which is of great significance in modern society where interpersonal relationships are increasingly alienated.

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Keywords

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