Purchase intent forecasting with convolutional hierarchical transformer networks

Abstract

Purchase intent forecasting, which aims to model user consumption behavior over different categories of items, plays a key role in many services, like online retailing systems, computational advertising and personalized recommendations. While the recently emerged deep neural network models (e.g., recurrent neural network, or attention mechanism) have been proposed to understand user's sequential behavior, we argue that the successes of these methods is largely rely on the data sufficiency. However, the practical purchase forecasting scenarios involve highly sparse data distributions across categories and time. In such cases, one has to deal with the data imbalance problem in order to encode the complex patterns of user purchase behaviors. To tackle this challenge, we develop a Convolutional Hierarchical TRansformer networks (CHTR), to enable the purchase pattern modeling with the multi-grained temporal dynamics, so as to alleviate the data imbalance issue. In our CHTR framework, we develop a multi-grained hierarchical transformer network, to make the learned behavior embeddings be reflective of the multi-level relational structures. Then, a dependency modeling component is proposed to aggregate the multi-relational context signals and capture the underlying dependent structures. Our experiments on real-world datasets show the significant improvements obtained by CHTR over different types of alternative methods.