对比11种检索技术，提升IT工单推荐准确率。
Comparison of Information Retrieval Techniques Applied to IT Support Tickets
- 用Sentence-BERT等11种方法比较工单检索效果。
- 多语言Sentence-BERT达78.7%推荐相关性，最优。
- 开源代码与数据，适合想落地工单系统者参考。
依赖IT服务的机构普遍重视帮助台系统的中心作用，作为连接技术人员与用户的服务请求枢纽。通过多种机器学习模型，帮助台系统可调用历史修正措施，但不同模型在不同数据集上表现各异。本文在一份IT支持工单数据集上对比了11种信息检索技术，目标是开发辅助技术支持分析员的软件。最佳结果来自多语言版Sentence-BERT（distiluse-base-multilingual-cased-v1），其78.7%的推荐被认定为相关。TF-IDF（69.0%）、Word2vec（68.7%）和LDA（66.3%）也表现稳定。研究还公开了数据集和核心代码，实现了一个最小可行原型，验证了系统实用性，并提出一种新评估指标，更贴近分析师对检索质量的主观判断。
原文摘要 · Abstract (English)
Institutions dependent on IT services and resources acknowledge the crucial significance of an IT help desk system, that act as a centralized hub connecting IT staff and users for service requests. Employing various Machine Learning models, these IT help desk systems allow access to corrective actions used in the past, but each model has different performance when applied to different datasets. This work compares eleven Information Retrieval techniques in a dataset of IT support tickets, with the goal of implementing a software that facilitates the work of Information Technology support analysts. The best results were obtained with the Sentence-BERT technique, in its multi-language variation distilluse-base-multilingual-cased-v1, where 78.7% of the recommendations made by the model were considered relevant. TF-IDF (69.0%), Word2vec (68.7%) and LDA (66.3%) techniques also had consistent results. Furthermore, the used datasets and essential parts of coding have been published and made open source. It also demonstrated the practicality of a support ticket recovery system by implementing a minimal viable prototype, and described in detail the implementation of the system. Finally, this work proposed a novel metric for comparing the techniques, whose aim is to closely reflect the perception of the IT analysts about the retrieval quality.
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