Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2021

Abstract

Online insurance is a new type of e-commerce with exponential growth. An effective recommendation model that maximizes the total revenue of insurance products listed in multiple customized sales scenarios is crucial for the success of online insurance business. Prior recommendation models are ineffective because they fail to characterize the complex relatedness of insurance products in multiple sales scenarios and maximize the overall conversion rate rather than the total revenue. Even worse, it is impractical to collect training data online for total revenue maximization due to the business logic of online insurance. We propose RevMan, a Revenue-aware Multi-task Network for online insurance recommendation. RevMan adopts an adaptive attention mechanism to allow effective feature sharing among complex insurance products and sales scenarios. It also designs an efficient offline learning mechanism to learn the rank that maximizes the expected total revenue, by reusing training data and model for conversion rate maximization. Extensive offline and online evaluations show that RevMan outperforms the state-of-the-art recommendation systems for e-commerce.

Keywords

Insurance, machine learning, e-commerce, complexity, recommender systems

Discipline

Databases and Information Systems | E-Commerce | Insurance | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual, February 2 - February 9

First Page

303

Last Page

310

Publisher

AAAi Press

City or Country

Palo Alto, CA

Embargo Period

8-30-2021

Copyright Owner and License

Publisher

Additional URL

https://ojs.aaai.org/index.php/AAAI/article/view/16105

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