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The E-commerce platform has become the principal battleground where peoplesearch, browse and pay for whatever they want. Critical as is to improve theonline shopping experience for customers and merchants, how to find a properapproach for user intent prediction are paid great attention in both industryand academia. In this paper, we propose a novel user intent prediction model,TPG-DNN, to complete the challenging task, which is based on adaptive gatedrecurrent unit (GRU) loss function with multi-task learning. We creatively usethe GRU structure and total probability formula as the loss function to modelthe users' whole online purchase process. Besides, the multi-task weightadjustment mechanism can make the final loss function dynamically adjust theimportance between different tasks through data variance. According to the testresult of experiments conducted on Taobao daily and promotion data sets, theproposed model performs much better than existing click through rate (CTR)models. At present, the proposed user intent prediction model has been widelyused for the coupon allocation, advertisement and recommendation on Taobaoplatform, which greatly improve the user experience and shopping efficiency,and benefit the gross merchandise volume (GMV) promotion as well.

TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning