基于两阶段动态兴趣识别的购买行为预测模型

Purchase behavior prediction model based on two-stage dynamic interest recognition

在线购买预测旨在预测用户的购买行为,为购物网站带来可观的商业价值。针对传统模型学习用户历史行为中隐含的兴趣偏好不准确的问题,提出基于两阶段动态兴趣识别的购买行为预测模型,以预测用户购买商品的概率。首先,模型的第一阶段构建用户-商品的点击频率图,并利用轻量图卷积网络(LightGCN)学习图的上下文特征作为用户的静态兴趣表征;其次,第二阶段采用带有注意力机制的双向门控递归单元(Bi-GRU)探索用户偏好的转化过程;最后,针对潜在的高维特征,建立一个融合动态兴趣和隐含特征的购买预测模型。在2个真实电子商务数据集上的实验结果表明,所提模型与图卷积网络(GCN)模型相比,准确率至少提升0.3个百分点,F1分数至少提升了2.05个百分点。

Online purchase prediction aims to predict users’purchase behaviors,which can generate considerable commercial value for shopping websites.To address the problem of traditional models’inability to learn implicit interest preferences from users’historical behaviors accurately,a two-stage dynamic interest recognition model for online purchase prediction was proposed to predict the probability of users purchasing products.Firstly,at the first stage of the model,a click frequency graph of user-product pairs was constructed,and Light-Graph Convolutional Network(LightGCN)was utilized to learn contextual features of the graph as the static interest’s representation of users.Then,at the second stage,Bidirectional Gated Recurrent Unit(Bi-GRU)with attention mechanism was applied to explore the transformation process of user preferences.Finally,aiming at the potential high-dimensional features,a purchase prediction model integrating dynamic interest and implicit features was built.The extensive experimental results on two real e-commerce datasets show that compared with Graph Convolutional Network(GCN)model,the proposed model has the accuracy improved by at least 0.3 percentage points,and the F1 score improved by at least 2.05 percentage points.