For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed and reliability of customer support for end users through automated ticket classification and answers selection for support representatives. Two machine learning and natural language processing techniques are demonstrated: one relying on feature engineering (COTA v1) and the other exploiting raw signals through deep learning architectures (COTA v2). COTA v1 employs a new approach that converts the multi-classification task into a ranking problem, demonstrating significantly better performance in the case of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a novel deep learning architecture that allows for heterogeneous input and output feature types and injection of prior knowledge through network architecture choices. This paper compares these models and their variants on the task of ticket classification and answer selection, showing model COTA v2 outperforms COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B test is conducted in a production setting validating the real-world impact of COTA in reducing issue resolution time by 10 percent without reducing customer satisfaction.
MIT Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）；Yale School of Management, Yale University（耶鲁大学管理学院）；Google Research（谷歌研究院）；Department of Management, University of Toronto Scarborough and Rotman School of Management（多伦多大学士嘉堡分校管理系与罗特曼管理学院）
Multiple Interest and Fine Granularity Network for User Modeling
Alibaba Group（阿里巴巴集团）
用户建模在工业推荐系统中起着基础性的作用，无论是在匹配阶段还是在排名阶段，无论是在客户体验还是业务收入方面。如何从用户的历史行为序列中有效地提取用户的多种兴趣，以提高推荐结果的相关性和个性化，是用户建模的一个难题。现有的基于深度学习的方法大多利用项目id和类别id，但忽略了颜色和材质等细粒度特征，这阻碍了对用户兴趣的细粒度建模。本文提出了多兴趣细粒度网络模型（Multiple interest and Fine granularity Net-work，MFN），该模型针对用户的多个细粒度兴趣，从用户多个兴趣之间的相似关系和组合关系两个方面进行建模，具体地说，对于相似关系的建模，我们利用了两组嵌入，其中一个是来自预训练模型（例如Glove）的固定嵌入以给出注意力权重，另一个是与MFN一起训练的可训练嵌入。为了对组合关系进行建模，利用自关注层来构建不同兴趣表示的高阶组合。在网络构建中，我们设计了一个兴趣提取模块，利用注意力机制从用户历史行为序列中捕获多种兴趣表征，并利用辅助损失来增强兴趣表征的区分度。然后利用层次网络对不同粒度的多个兴趣向量与目标项之间的注意关系进行建模。我们评估MFNon公共和工业数据集。实验结果表明，所提出的最大似然数表示方法比现有的表示方法具有更好的性能。
