تقرير
Contextual Bandit Applications in Customer Support Bot
| العنوان: | Contextual Bandit Applications in Customer Support Bot |
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| المؤلفون: | Sajeev, Sandra, Huang, Jade, Karampatziakis, Nikos, Hall, Matthew, Kochman, Sebastian, Chen, Weizhu |
| المصدر: | KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (August 2021) Pages 3522-3530 |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, I.2.0 |
| الوصف: | Virtual support agents have grown in popularity as a way for businesses to provide better and more accessible customer service. Some challenges in this domain include ambiguous user queries as well as changing support topics and user behavior (non-stationarity). We do, however, have access to partial feedback provided by the user (clicks, surveys, and other events) which can be leveraged to improve the user experience. Adaptable learning techniques, like contextual bandits, are a natural fit for this problem setting. In this paper, we discuss real-world implementations of contextual bandits (CB) for the Microsoft virtual agent. It includes intent disambiguation based on neural-linear bandits (NLB) and contextual recommendations based on a collection of multi-armed bandits (MAB). Our solutions have been deployed to production and have improved key business metrics of the Microsoft virtual agent, as confirmed by A/B experiments. Results include a relative increase of over 12% in problem resolution rate and relative decrease of over 4% in escalations to a human operator. While our current use cases focus on intent disambiguation and contextual recommendation for support bots, we believe our methods can be extended to other domains. Comment: in KDD 2021 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1145/3447548.3467165 |
| URL الوصول: | http://arxiv.org/abs/2112.03210 |
| رقم الأكسشن: | edsarx.2112.03210 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1145/3447548.3467165 |
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