Customer service complaint work order classification based on matrix factorization and attention multi-task learning
The automatic classification of complaint work orders is the requirement of the digital and intelligent development of customer service of communication operators.The categories of customer service complaint work orders have multiple levels, each level has multiple labels, and the levels are related...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-02-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022031/ |
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| _version_ | 1841529018179911680 |
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| author | Yong SONG Zhiwei YAN Yukun QIN Dongming ZHAO Xiaozhou YE Yuanyuan CHAI Ye OUYANG |
| author_facet | Yong SONG Zhiwei YAN Yukun QIN Dongming ZHAO Xiaozhou YE Yuanyuan CHAI Ye OUYANG |
| author_sort | Yong SONG |
| collection | DOAJ |
| description | The automatic classification of complaint work orders is the requirement of the digital and intelligent development of customer service of communication operators.The categories of customer service complaint work orders have multiple levels, each level has multiple labels, and the levels are related, which belongs to a typical hierarchical multi-label text classification (HMTC) problem.Most of the existing solutions are based on classifiers to process all classification labels at the same time, or use multiple classifiers for each level, ignoring the dependence between hierarchies.A matrix factorization and attention-based multi-task learning approach (MF-AMLA) to deal with hierarchical multi-label text classification tasks was proposed.Under the classification data of real complaint work orders in the customer service scenario of communication operators, the maximum Top1 F1 value of MF-AMLA is increased by 21.1% and 5.7% respectively compared with the commonly used machine learning algorithm and deep learning algorithm in this scenario.It has been launched in the customer service system of one mobile operator, the accuracy of model output is more than 97%, and the processing efficiency of customer service agent unit time has been improved by 22.1%. |
| format | Article |
| id | doaj-art-694380af6206470f9181b1d5fba6fa8b |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-02-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-694380af6206470f9181b1d5fba6fa8b2025-01-15T03:26:43ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-02-013810311059809431Customer service complaint work order classification based on matrix factorization and attention multi-task learningYong SONGZhiwei YANYukun QINDongming ZHAOXiaozhou YEYuanyuan CHAIYe OUYANGThe automatic classification of complaint work orders is the requirement of the digital and intelligent development of customer service of communication operators.The categories of customer service complaint work orders have multiple levels, each level has multiple labels, and the levels are related, which belongs to a typical hierarchical multi-label text classification (HMTC) problem.Most of the existing solutions are based on classifiers to process all classification labels at the same time, or use multiple classifiers for each level, ignoring the dependence between hierarchies.A matrix factorization and attention-based multi-task learning approach (MF-AMLA) to deal with hierarchical multi-label text classification tasks was proposed.Under the classification data of real complaint work orders in the customer service scenario of communication operators, the maximum Top1 F1 value of MF-AMLA is increased by 21.1% and 5.7% respectively compared with the commonly used machine learning algorithm and deep learning algorithm in this scenario.It has been launched in the customer service system of one mobile operator, the accuracy of model output is more than 97%, and the processing efficiency of customer service agent unit time has been improved by 22.1%.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022031/hierarchical multi-label classificationattention mechanismmulti-task learningcustomer service work order classification |
| spellingShingle | Yong SONG Zhiwei YAN Yukun QIN Dongming ZHAO Xiaozhou YE Yuanyuan CHAI Ye OUYANG Customer service complaint work order classification based on matrix factorization and attention multi-task learning Dianxin kexue hierarchical multi-label classification attention mechanism multi-task learning customer service work order classification |
| title | Customer service complaint work order classification based on matrix factorization and attention multi-task learning |
| title_full | Customer service complaint work order classification based on matrix factorization and attention multi-task learning |
| title_fullStr | Customer service complaint work order classification based on matrix factorization and attention multi-task learning |
| title_full_unstemmed | Customer service complaint work order classification based on matrix factorization and attention multi-task learning |
| title_short | Customer service complaint work order classification based on matrix factorization and attention multi-task learning |
| title_sort | customer service complaint work order classification based on matrix factorization and attention multi task learning |
| topic | hierarchical multi-label classification attention mechanism multi-task learning customer service work order classification |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022031/ |
| work_keys_str_mv | AT yongsong customerservicecomplaintworkorderclassificationbasedonmatrixfactorizationandattentionmultitasklearning AT zhiweiyan customerservicecomplaintworkorderclassificationbasedonmatrixfactorizationandattentionmultitasklearning AT yukunqin customerservicecomplaintworkorderclassificationbasedonmatrixfactorizationandattentionmultitasklearning AT dongmingzhao customerservicecomplaintworkorderclassificationbasedonmatrixfactorizationandattentionmultitasklearning AT xiaozhouye customerservicecomplaintworkorderclassificationbasedonmatrixfactorizationandattentionmultitasklearning AT yuanyuanchai customerservicecomplaintworkorderclassificationbasedonmatrixfactorizationandattentionmultitasklearning AT yeouyang customerservicecomplaintworkorderclassificationbasedonmatrixfactorizationandattentionmultitasklearning |