用机器学习自动推荐工单解决方案，提升服务效率。
A Scalable and High Availability Solution for Recommending Resolutions to Problem Tickets
- 融合聚类与深度学习，识别相似工单并推荐解决方法。
- 在开源与电信专有数据集上准确率均超90%。
- 支持实时部署，适合高可用生产环境使用。
工单处理是各行业服务中的常见任务，尤其在电信计费系统中尤为关键。机器学习可通过分析历史工单数据中的模式，自动识别并推荐解决方案。然而，数据漂移、缺失数据、过往解决记录不足、自由文本导致的相似方案混淆等问题使该过程复杂化。本文提出一种鲁棒的ML驱动方案，结合聚类、监督学习及先进NLP模型（如LDA、Siamese网络、One-shot学习、索引嵌入）有效应对上述挑战。基于前期工作，我们展示了基于聚类的解决方案识别方法，并在分类任务中应用多种模型。此外，构建了实时仪表盘和基于Kubernetes的高可用生产部署系统。在开源Bitext客户支持数据集及专有电信数据集上的实验表明，该方案具备高预测准确性。
原文摘要 · Abstract (English)
Resolution of incidents or problem tickets is a common theme in service industries in any sector, including billing and charging systems in telecom domain. Machine learning can help to identify patterns and suggest resolutions for the problem tickets, based on patterns in the historical data of the tickets. However, this process may be complicated due to a variety of phenomena such as data drift and issues such as missing data, lack of data pertaining to resolutions of past incidents, too many similar sounding resolutions due to free text and similar sounding text. This paper proposes a robust ML-driven solution employing clustering, supervised learning, and advanced NLP models to tackle these challenges effectively. Building on previous work, we demonstrate clustering-based resolution identification, supervised classification with LDA, Siamese networks, and One-shot learning, Index embedding. Additionally, we present a real-time dashboard and a highly available Kubernetes-based production deployment. Our experiments with both the open-source Bitext customer-support dataset and proprietary telecom datasets demonstrate high prediction accuracy.
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