RevMan: Revenue-aware Multi-task Online Insurance Recommendation

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 Revenueaware 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-theart recommendation systems for e-commerce.

[1]  Hitoshi Iyatomi,et al.  Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives , 2019, KDD.

[2]  Yu Zhang,et al.  A Survey on Multi-Task Learning , 2017, IEEE Transactions on Knowledge and Data Engineering.

[3]  Wenhao Zhang,et al.  Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning , 2019, WWW.

[4]  Yu Zhang,et al.  CoNet: Collaborative Cross Networks for Cross-Domain Recommendation , 2018, UMCit@KDD.

[5]  Xiao Ma,et al.  Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate , 2018, SIGIR.

[6]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[7]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[8]  Yiqun Liu,et al.  Economic Recommendation with Surplus Maximization , 2016, WWW.

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  Huifeng Guo,et al.  PAL: a position-bias aware learning framework for CTR prediction in live recommender systems , 2019, RecSys.

[11]  Zhe Zhao,et al.  Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts , 2018, KDD.

[12]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[13]  Yu Gong,et al.  A Minimax Game for Instance based Selective Transfer Learning , 2019, KDD.

[14]  Ed H. Chi,et al.  SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning , 2019, AAAI.

[15]  Peng Jiang,et al.  Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems , 2019, WWW 2019.

[16]  Ya Zhang,et al.  Multi-task learning for boosting with application to web search ranking , 2010, KDD.

[17]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[18]  Martin Wattenberg,et al.  Ad click prediction: a view from the trenches , 2013, KDD.

[19]  Qiang Yang,et al.  An Overview of Multi-task Learning , 2018 .

[20]  Sebastian Ruder,et al.  An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.

[21]  Ronald J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[22]  Minyi Guo,et al.  Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation , 2019, WWW.

[23]  Ye Bi,et al.  A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users , 2020, SIGIR.

[24]  John Langford,et al.  Doubly Robust Policy Evaluation and Learning , 2011, ICML.

[25]  Yongfeng Zhang,et al.  Maximizing Marginal Utility per Dollar for Economic Recommendation , 2019, WWW.

[26]  Nick Craswell,et al.  An experimental comparison of click position-bias models , 2008, WSDM '08.

[27]  Qi Zhao,et al.  Multi-Product Utility Maximization for Economic Recommendation , 2017, WSDM.

[28]  Hongyuan Zha,et al.  Multi-task learning for learning to rank in web search , 2009, CIKM.

[29]  Yunming Ye,et al.  DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.

[30]  Aaron Flores,et al.  Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising , 2019, KDD.