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.
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References
Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading Wikipedia to answer open-domain questions. In: ACL, pp. 1870–1879 (2017)
Chen, Q., Zhu, X., Ling, Z.H., Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: ACL, pp. 1657–1668 (2017)
Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10 (2016)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: ACL, pp. 4171–4186 (2019)
Eales-Reynolds, L.J., Clarke, C.: Impact of a novel training experience on the development of a customer service culture in a large hospital trust. Int. J. Health Care Qual. Assur. 25, 483–497 (2012)
Ebesu, T., Shen, B., Fang, Y.: Collaborative memory network for recommendation systems. In: SIGIR, pp. 515–524 (2018)
Fukuda, T., Suzuki, M., Kurata, G., Thomas, S., Cui, J., Ramabhadran, B.: Efficient knowledge distillation from an ensemble of teachers. In: Interspeech, pp. 3697–3701 (2017)
Gong, Y., Luo, H., Zhang, J.: Natural language inference over interaction space. In: ICLR (2018)
Guo, J., Fan, Y., Ai, Q., Croft, W.B.: A deep relevance matching model for ad-hoc retrieval. In: CIKM, pp. 55–64 (2016)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)
Hinton, G., Oriol, V., Jeff, D.: Distilling the knowledge in a neural network. In: NIPS Deep Learning and Representation Learning Workshop (2015)
Hui, K., Yates, A., Berberich, K., De Melo, G.: Co-PACRR: a context-aware neural IR model for ad-hoc retrieval. In: WSDM, pp. 279–287, February 2018
Iyer, S., Dandekar, N., Csernai, K.: First quora dataset release: Question pairs (2017). https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs
Kaufman, R.: Why your customer service training won’t lead to happy customers (or inspired employees). J. Qual. Particip. 37, 33 (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Kresch, M.: What is intelligent customer service (2016). https://cloudblogs.microsoft.com/dynamics365/bdm/2016/01/19/what-is-intelligent-customer-service. Accessed 12 May 2020
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119 (2013)
Mirchandani, K.: Learning racial hierarchies: communication skills training in transnational customer service work. J. Workplace Learn. 24, 338–350 (2012)
Peters, M., et al.: Deep contextualized word representations. In: NAACL, pp. 2227–2237 (2018)
Rao, J., Liu, L., Tay, Y., Yang, W., Shi, P., Lin, J.: Bridging the gap between relevance matching and semantic matching for short text similarity modeling. In: EMNLP-IJCNLP, pp. 5373–5384 (2019)
Rao, J., Yang, W., Zhang, Y., Ture, F., Lin, J.: Multi-perspective relevance matching with hierarchical convnets for social media search. In: AAAI, pp. 232–240 (2019)
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. In: ICLR (2015)
Sari, P.K., Alamsyah, A., Wibowo, S.: Measuring e-commerce service quality from online customer review using sentiment analysis. In: Journal of Physics: Conference Series, p. 012053 (2018)
Sun, X., Ma, X., Ni, Z., Bian, L.: A new LSTM network model combining TextCNN. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 416–424. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04167-0_38
Tang, R., Lu, Y., Liu, L., Mou, L., Vechtomova, O., Lin, J.: Distilling task-specific knowledge from Bert into simple neural networks. arXiv preprint arXiv:1903.12136, March 2019
Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: KDD, pp. 1235–1244 (2015)
Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences. In: IJCAI, pp. 4144–4150 (2017)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: NIPS, pp. 5754–5764 (2019)
You, S., Xu, C., Xu, C., Tao, D.: Learning from multiple teacher networks. In: KDD, pp. 1285–1294 (2017)
Zhou, X., et al.: Multi-turn response selection for chatbots with deep attention matching network. In: ACL, pp. 1118–1127 (2018)
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Fu, M. et al. (2020). ICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_26
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