Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction

• URL: http://arxiv.org/abs/2104.09713v1
• Date: Tue, 20 Apr 2021 01:45:06 GMT
• Title: Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
• Authors: Hong Wen and Jing Zhang and Fuyu Lv and Wentian Bao and Tianyi Wang and Zulong Chen
• Abstract summary: Conversion Rate (emphCVR) prediction in modern industrial e-commerce platforms is becoming increasingly important. We propose a novel emphCVR prediction method by Hierarchically Modeling both Micro and Macro behaviors. $HM3$ can be trained end-to-end and address the emph SSB and emphDS issues.
• Score: 14.494225676311448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversion Rate (\emph{CVR}) prediction in modern industrial e-commerce platforms is becoming increasingly important, which directly contributes to the final revenue. In order to address the well-known sample selection bias (\emph{SSB}) and data sparsity (\emph{DS}) issues encountered during CVR modeling, the abundant labeled macro behaviors ($i.e.$, user's interactions with items) are used. Nonetheless, we observe that several purchase-related micro behaviors ($i.e.$, user's interactions with specific components on the item detail page) can supplement fine-grained cues for \emph{CVR} prediction. Motivated by this observation, we propose a novel \emph{CVR} prediction method by Hierarchically Modeling both Micro and Macro behaviors ($HM^3$). Specifically, we first construct a complete user sequential behavior graph to hierarchically represent micro behaviors and macro behaviors as one-hop and two-hop post-click nodes. Then, we embody $HM^3$ as a multi-head deep neural network, which predicts six probability variables corresponding to explicit sub-paths in the graph. They are further combined into the prediction targets of four auxiliary tasks as well as the final $CVR$ according to the conditional probability rule defined on the graph. By employing multi-task learning and leveraging the abundant supervisory labels from micro and macro behaviors, $HM^3$ can be trained end-to-end and address the \emph{SSB} and \emph{DS} issues. Extensive experiments on both offline and online settings demonstrate the superiority of the proposed $HM^3$ over representative state-of-the-art methods.

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