Abstract
As an promising technology to improve the network configuration efficiency, IBN (Intent-Based Network) can realize the automatic configuration from users to devices, with the objective to reduce the maintenance costs and improve the stability of system. In the current IBN, the NER (Named Entity Recognition) model is used to extract intents from the natural language information input by the user in form of text or voice. However, traditional intent extraction method requires the user to be familiar with specific network services, which means that users must have certain prior knowledge. It is difficult to completely and clearly extract the true intention of the user in real deployment applications. To this end, this paper proposes the Session based Recommender (SBR) based user intent extraction mechanism for IBN which utilizes the session information between user and system to help users choose the most suitable network configuration scheme. In this way, the proposed model can ensure the integrity and reliability of user intent extraction. Finally, some commonly used SBR models are evaluated on the Diginetica dataset. The results show that these SBR models have good capabilities in the task of session recommendation and illustrate the feasibility of combining IBN with SBR.
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Acknowledgment
This research was supported by the National Key Research and Development Program (2018YFE0206800). Jinsuo Jia, Xiaochen Liang, Peng Xu, Weiwei Li and Yizhong Hu contributed equally to this work. Corresponding author is Jianfeng Guan. The authors would like to thank the anonymous reviewers for their valuable comments which helped them to improve the content, organization, and presentation of this paper.
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Jia, J., Liang, X., Xu, P., Li, W., Hu, Y., Guan, J. (2023). SBR-based User Intent Extraction Mechanism for Intent-Based Network. In: Quan, W. (eds) Emerging Networking Architecture and Technologies. ICENAT 2022. Communications in Computer and Information Science, vol 1696. Springer, Singapore. https://doi.org/10.1007/978-981-19-9697-9_5
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