Application of intelligent sorting of customer service work orders with multiple factors vectorization

Hua REN; Zheng WANG; Shaomin WANG

doi:10.11959/j.issn.1000-0801.2018305

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Application of intelligent sorting of customer service work orders with multiple factors vectorization

Column:artificial intelligence technology and application | 更新时间：2024-06-05

- Application of intelligent sorting of customer service work orders with multiple factors vectorization
- Telecommunications Science Vol. 34, Issue 12, Pages: 125-131(2018)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2018305
  CLC： TP391
- Published Online：2018-12，
  
  Published：20 December 2018
- 稿件说明：
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Hua REN, Zheng WANG, Shaomin WANG. Application of intelligent sorting of customer service work orders with multiple factors vectorization[J]. Telecommunications science, 2018, 34(12): 125-131.
DOI：

Hua REN, Zheng WANG, Shaomin WANG. Application of intelligent sorting of customer service work orders with multiple factors vectorization[J]. Telecommunications science, 2018, 34(12): 125-131. DOI： 10.11959/j.issn.1000-0801.2018305.

摘要

客服工单作为互联网行业客户最重要的接触点，智能化、智慧化处理成为各行业重点关注并亟待解决的主要问题，工单的智能排序为解决这一问题提供了可行化应用探索。人工智能及大数据的飞速发展给解决此类问题提供了新的方向和契机。首先分析了客服工单智能化排序的需求背景，在此基础上对实现客服工单智能化排序的主要方法和实现过程进行了分析，阐述了基于多因素向量化客服工单智能排序的应用探索。

Abstract

As the most important contact point for customers in the internet industry

customer service orders have become the main issues that need to be addressed and urgently solved in various industries.The intelligent sequencing of work orders provides a feasible application for solving this problem.The rapid development of artificial intelligence technology and big data technology has given new directions and opportunities to solve such problems.The background of the intelligent sequencing of customer service orders was analyzed firstly.On the basis of this

the main methods and implementations of intelligent sequencing of customer service orderswere analyzed

and the application of intelligent sequencing of customer service orders based on multi-factor vectorization was expounded.

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references

饶竹一 , 张云翔 . 基于知识图谱的智能客服系统研究 [J ] . 电力信息与通信技术 ,2095-641X(2017)07-0041-05.

RAO Z Y , ZHANG Y X . Research on intelligent customer service system based on knowledge map [J ] . Electric Power ICT 2095-641X(2017)07-0041-05.

李文芳 , 梁雅洁 , 高玉华 , 等 . 95598 业务支持系统工单识别与分配技术研究 [J ] . 山东电力技术 ,1007-9904(20 16)03-0071-04.

LI W F , LIANG Y J , GAO Y H , et al . Technology research of 95598 business support system work order recognition and distribution [J ] . Shandong Dianli Jishu 2095-641X(2017)07-0041-05.

刘建 , 赵加奎 , 杨维 , 等 . 电力 95598 客户服务质检抽样算法研究 [J ] . 电网技术 ,1000-3673(2015)11-3163-06.

LIU J , ZHAO J K , YANG W , et al . Research on quality inspection sampling algorithm of electric 95598 customer service [J ] . Power System Technology 1000-3673（2015）11-3163-06.

顾斌 , 彭涛 , 车伟 . 基于词典扩充的电力客服工单情感倾向性分析 [J ] . 现代电子技术 ,1004⁃373X(2017)11⁃0163⁃04.

GU B , PENG T , CHE W . Dictionary expansion based sentiment tendency analysis of power customer service order [J ] . Modern Electronics Technique 1004⁃373X（2017）11⁃0163⁃04.

李颢 , 张吉皓 . 基于文本挖掘技术的客服投诉工单自动分类探讨 [J ] . 移动通信 , 2017 , 41 ( 23 ): 66 - 72 .

LI H , ZHANG J H . Discussion of automatic classification of customer service compla7intsbased on text mining [J ] . Mobile communication , 2017 , 41 ( 23 ): 66 - 72 .

蔡鑫 , 娄京生 . 基于LSTM深度学习模型的中国电信官方微博用户情绪分析 [J ] . 电信科学 , 2017 , 33 ( 12 ): 138 - 147 .

CAI X , LOU J S . Sentiment analysis of telecom official micro-blog users based on LSTM deep learning model [J ] . Telecommunications Science , 2017 , 33 ( 12 ): 136 - 141 .

曹亚男 , 赵璐 , 裴海超 , 等 . 学习型智能交互系统的研究与实现 [J ] . 电子科学技术 ,2095-8595(2017)03-133-005.

CAO Y N , ZHAO L , PEI H C , et al . Research and implementation on learning intelligent interaction system [J ] . Electronic Science ＆ Technology ,2095-8595(2017)03-133-005.

厉建宾 , 朱雅魁 , 付立衡 . 基于大数据技术的客户诉求分析与应用 [J ] . 电力大数据 ,1008－083X(2017)10－0014－04.

LI J B , ZHU Y K , FU L H . The customer demands analysis and application based on big data [J ] . Power Systems and Big Data ,1008－083X(2017)10－0014－04.

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