TPG-DNN：一种基于全概率公式和GRU损失的多任务学习用户意图预测方法
TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning
摘要：
电子商务平台已经成为人们搜索、浏览和支付任何他们想要的东西的主要战场。如何找到一种合适的用户意图预测方法是提高消费者和商家在线购物体验的关键，也是业界和学术界关注的焦点。在本文中，我们提出了一种新的用户意图预测模型，TPG-DNN，以完成具有挑战性的任务，这是基于自适应门控递归单元（GRU）损失函数与多任务学习。我们创造性地使用GRU结构和全概率公式作为损失函数，对用户的整个在线购买过程进行建模。此外，多任务权重调整机制可以使最终的损失函数通过数据方差动态调整不同任务之间的重要性。通过对淘宝每日数据集和促销数据集的实验结果表明，该模型的性能明显优于现有的点击率模型。目前，本文提出的用户意图预测模型已被广泛应用于淘宝平台的优惠券分配、广告投放和推荐等方面，极大地提升了用户体验和购物效率，同时也有利于商品总销量（GMV）的提升。
英文摘要：
The E-commerce platform has become the principal battleground where people search, browse and pay for whatever they want. Critical as is to improve the online shopping experience for customers and merchants, how to find a proper approach for user intent prediction are paid great attention in both industry and academia. In this paper, we propose a novel user intent prediction model, TPG-DNN, to complete the challenging task, which is based on adaptive gated recurrent unit (GRU) loss function with multi-task learning. We creatively use the GRU structure and total probability formula as the loss function to model the users' whole online purchase process. Besides, the multi-task weight adjustment mechanism can make the final loss function dynamically adjust the importance between different tasks through data variance. According to the test result of experiments conducted on Taobao daily and promotion data sets, the proposed model performs much better than existing click through rate (CTR) models. At present, the proposed user intent prediction model has been widely used for the coupon allocation, advertisement and recommendation on Taobao platform, which greatly improve the user experience and shopping efficiency, and benefit the gross merchandise volume (GMV) promotion as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
