POSGen: Personalized Opening Sentence Generation for Online Insurance Sales
| Autor: | Li, Yu, Zhang, Yi, Wu, Weijia, Zhou, Zimu, Li, Qiang |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 2022 IEEE International Conference on Big Data (Big Data). |
| DOI: | 10.1109/bigdata55660.2022.10020230 |
| Popis: | The insurance industry is shifting their sales mode from offline to online, in expectation to reach massive potential customers in the digitization era. Due to the complexity and the nature of insurance products, a cost-effective online sales solution is to exploit chatbot AI to raise customers' attention and pass those with interests to human agents for further sales. For high response and conversion rates of customers, it is crucial for the chatbot to initiate a conversation with personalized opening sentences, which are generated with user-specific topic selection and ordering. Such personalized opening sentence generation is challenging because (i) there are limited historical samples for conversation topic recommendation in online insurance sales and (ii) existing text generation schemes often fail to support customized topic ordering based on user preferences. We design POSGen, a personalized opening sentence generation scheme dedicated for online insurance sales. It transfers user embeddings learned from auxiliary online user behaviours to enhance conversation topic recommendation, and exploits a context management unit to arrange the recommended topics in user-specific ordering for opening sentence generation. POSGen is deployed on a real-world online insurance platform. It achieves 2.33x total insurance premium improvement through a two-month global test. IEEE BigData 2022 |
| Databáze: | OpenAIRE |
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