Journal of South China University of Technology(Natural Science) >

2025 , Vol. 53 >Issue 3: 40 - 49

DOI: https://doi.org/10.12141/j.issn.1000-565X.240191

Computer Science & Technology

Customer Service Knowledge Recommendation Large Model Construction Driven by Intent Understanding

  • MA Xiaoliang ,
  • GAO Jie ,
  • LIU Ying ,
  • PEI Qingqi ,
  • ZHAO Ruqiang ,
  • YANG Bangxing ,
  • DENG Congjian
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  • 1.Guangzhou Institue of Technology，Xidian University，Guangzhou 510555，Guangdong，China
    2.Guangzhou Branch of China Telecom Co. ，Ltd. ，Guangzhou 510620，Guangdong，China
    3.MA Xiaoliang’?s Model Worker and Innovative Craftsman Workshop，Guangzhou 510620，Guangdong，China
    4.Guangzhou Yunqu Information Technology Co. ，Ltd. ，Guangzhou 510665，Guangdong，China
    5.China DataCom Co. ，Ltd. ，Guangzhou 510650，Guangdong，China
马晓亮（1973—），男，博士生，高级工程师，华南理工大学工商管理学院讲席教授，主要从事人工智能、自然语言处理、方言处理、运营商客服运营、数据安全保护研究。E-mail： maxiaol.gd@chinatelecom.cn

Received date: 2024-04-17

  Online published: 2024-08-23

Supported by

the National Key R & D Program of China(2021YFB2700600)

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Abstract

With the deepening application of artificial intelligence technology in the field of customer service, telecommunications operators have raised higher standards for the accuracy of AI service knowledge recommendations. To enhance the efficiency and accuracy of knowledge recommendation in telecommunications operators’AI customer service systems, this paper proposed a large-scale customer service knowledge recommendation model driven by intent understanding. Firstly, the synonym and dialogue sequence keyword extraction model was employed to identify key terms in user queries. These keywords were then matched with questions in a standard question bank using semantic similarity comparison techniques to generate the most relevant standard questions. Additionally, a generative agent technology framework was utilized to construct and enrich the standard question bank, enabling the automatic generation of knowledge questions. The extracted standard questions were input into the ChatGLM2-6B large language model, which has been pre-trained and aligned with human preferences, further improving the accuracy of knowledge recommendations. The experimental results show that after the introduction of the standard question bank, the accuracy of the intelligent recommendation system in specific industry knowledge domains significantly increased from 74.8% to 85.9%. Multiple sets of comparative experimental results further validate the effectiveness of the strategy of establishing a standard question bank in improving accuracy. The large model discussed in this paper optimized the intelligent knowledge recommendation for operator AI customer service, providing new ideas and technical support for the knowledge recommendation in telecommunications operators’AI customer service systems. With this model, operators can more effectively understand and respond to customer inquiries, significantly enhancing the customer service experience.

Key words： intent understanding; AI customer service; large language model; keyword-based questioning; prompt example

Cite this article

MA Xiaoliang , GAO Jie , LIU Ying , PEI Qingqi , ZHAO Ruqiang , YANG Bangxing , DENG Congjian . Customer Service Knowledge Recommendation Large Model Construction Driven by Intent Understanding[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(3) : 40 -49 . DOI: 10.12141/j.issn.1000-565X.240191

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