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https://github.com/primaprashant/ai-customer-support

📚 Curated collection of blogs and papers on how different companies are using machine learning in production for better customer support.
https://github.com/primaprashant/ai-customer-support

ai applied-data-science applied-machine-learning applied-ml artificial-intelligence customer-service customer-support data-science deep-learning machine-learning natural-language-processing nlp paper production tech-blog

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📚 Curated collection of blogs and papers on how different companies are using machine learning in production for better customer support.

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# ai-customer-support



A curated collection of blogs and papers on **machine learning in production** for **customer support**.



[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](./CONTRIBUTING.md) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)



Title | Company | Year | Type

------|---------|------|-----

[How AI Text Generation Models Are Reshaping Customer Support at Airbnb](https://medium.com/airbnb-engineering/how-ai-text-generation-models-are-reshaping-customer-support-at-airbnb-a851db0b4fa3) | Airbnb | 2022 | blog

[Using Sentiment Score to Assess Customer Service Quality](https://medium.com/airbnb-engineering/using-sentiment-score-to-assess-customer-service-quality-43434dbe199b) | Airbnb | 2021 | blog

[Task-Oriented Conversational AI in Airbnb Customer Support](https://medium.com/airbnb-engineering/task-oriented-conversational-ai-in-airbnb-customer-support-5ebf49169eaa) | Airbnb | 2021 | blog

[How We Improved Agent Chat Efficiency with Machine Learning](https://engineering.grab.com/how-we-improved-agent-chat-efficiency-with-ml) | Grab |2021 | blog

[ML and customer support (Part 1): Using Machine Learning to enable world-class customer support](https://medium.com/data-science-at-microsoft/ml-and-customer-support-part-1-using-machine-learning-to-enable-world-class-customer-support-c90b3b02f6a3) | Microsoft | 2021 | blog

[ML and customer support (Part 2): Leveraging topic modeling to identify the top investment areas in support cases](https://medium.com/data-science-at-microsoft/ml-and-customer-support-part-2-leveraging-topic-modeling-to-identify-the-top-investment-areas-in-f0348382c251) | Microsoft | 2021 | blog

[A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents](https://arxiv.org/abs/2106.07381) | Amazon | 2021 | paper

[How Machine Learning Is Improving U.S. Navy Customer Support](https://ojs.aaai.org/index.php/AAAI/article/view/7023) | U.S. Navy | 2020 | paper

[Customer Support Ticket Escalation Prediction using Feature Engineering](https://arxiv.org/abs/2010.06145) | IBM | 2020 | paper

[Analyzing HPC Support Tickets: Experience and Recommendations](https://arxiv.org/abs/2010.04321) | Los Alamos National Laboratory | 2020 | paper

[Lessons from Contextual Bandit Learning in a Customer Support Bot](https://arxiv.org/abs/1905.02219) | Microsoft | 2019 | paper

[How natural language processing helps LinkedIn members get support easily](https://engineering.linkedin.com/blog/2019/04/how-natural-language-processing-help-support) | LinkedIn | 2019 | blog

[The science behind consolidating Answer Bot production Models: Part 1](https://zendesk.engineering/the-science-behind-consolidating-answer-bot-production-models-part-1-be5579a9047e) | Zendesk | 2019 | blog

[The science behind consolidating Answer Bot production Models: Part 2](https://zendesk.engineering/the-science-behind-consolidating-answer-bot-production-models-part-2-e4bb62233c0b) | Zendesk | 2019 | blog

[Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps](https://eng.uber.com/nlp-deep-learning-uber-maps/) | Uber | 2018 | blog

[COTA: Improving Uber Customer Care with NLP & Machine Learning](https://eng.uber.com/cota/) | Uber | 2018 | blog

[Scaling Uber’s Customer Support Ticket Assistant (COTA) System with Deep Learning](https://eng.uber.com/cota-v2/) | Uber | 2018 | blog

[COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks](https://arxiv.org/abs/1807.01337) | Uber | 2018 | paper

[Attention Fusion Networks: Combining Behavior and E-mail Content to Improve Customer Support](https://arxiv.org/abs/1811.03169) | Square | 2018 | paper

[Building user confidence in machine learning products](https://medium.com/zendesk-engineering/building-user-confidence-in-machine-learning-products-9b342d4b31c6) | Zendesk | 2018 | blog

[Making the most of Answer Bot: topic group visualization using t-SNE](https://medium.com/zendesk-engineering/making-the-most-of-answer-bot-topic-group-visualization-using-t-sne-fc5ea5bf5f34) | Zendesk | 2018 | blog

[Machine learning implementation strategy for a customer service center](https://cloudblogs.microsoft.com/dynamics365/bdm/2018/02/07/machine-learning-implementation-strategy-for-a-customer-service-center/) | Microsoft | 2018 | blog

[Hi, how can I help you?: Automating enterprise IT support help desks](https://arxiv.org/abs/1711.02012) | IBM | 2017 | paper

[SuperAgent: A Customer Service Chatbot for E-commerce Websites](https://aclanthology.org/P17-4017.pdf) | Microsoft | 2017 | paper