ICS-assist: intelligent customer inquiry resolution recommendation in online customer service for large e-commerce businesses

Min Fu, Jiwei Guan, Xi Zheng, Jie Zhou*, Jianchao Lu, Tianyi Zhang, Shoujie Zhuo, Lijun Zhan, Jian Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

Efficient and appropriate online customer service is essential to large e-commerce businesses. Existing solution recommendation methods for online customer service are unable to determine the best solutions at runtime, leading to poor satisfaction of end customers. This paper proposes a novel intelligent framework, called ICS-Assist, to recommend suitable customer service solutions for service staff at runtime. Specifically, we develop a generalizable two-stage machine learning model to identify customer service scenarios and determine customer service solutions based on a scenario-solution mapping table. A novel knowledge distillation network called “Panel-Student” is proposed to derive a small yet efficient distilled learning model. We implement ICS-Assist and evaluate it using an over 6-month field study with Alibaba Group. In our experiment, over 12,000 customer service staff use ICS-Assist to serve for over 230,000 cases per day on average. The experimental results show that ICS-Assist significantly outperforms the traditional manual method, and improves the solution acceptance rate, the solution coverage rate, the average service time, the customer satisfaction rate, and the business domain catering rate by up to 16%, 25%, 6%, 14% and 17% respectively, compared to the state-of-the-art methods.

Original languageEnglish
Title of host publicationService-Oriented Computing
Subtitle of host publication18th International Conference, ICSOC 2020 Dubai, United Arab Emirates, December 14–17, 2020 Proceedings
EditorsEleanna Kafeza, Boualem Benatallah, Fabio Martinelli, Hakim Hacid, Athman Bouguettaya, Hamid Motahari
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages370-385
Number of pages16
ISBN (Electronic)9783030653101
ISBN (Print)9783030653095
DOIs
Publication statusPublished - 2020
Event18th International Conference on Service-Oriented Computing, ICSOC 2020 - Dubai, United Arab Emirates
Duration: 14 Dec 202017 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12571 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Service-Oriented Computing, ICSOC 2020
CountryUnited Arab Emirates
CityDubai
Period14/12/2017/12/20

Keywords

  • Intelligent customer service
  • Natural language processing
  • Deep learning
  • Distilled learning

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