Purchase-related micro-behaviors, e.g., favorite, add to cart, read reviews, etc., provide implicit feedback of users' decision-making process. Such informative feedback can lead to fine-grained post-click conversion rate (CVR) modeling of the buying process. However, most existing works on CVR estimation either neglect these informative feedback, or model them as a sequential pattern with Recurrent Neural Networks. We argue such modeling could be inappropriate since different orders of micro-behaviors may represent similar user buying intention, and micro-behaviors often correlate with each other.

To this end, we propose to represent user micro-behaviors as a Purchase-related Micro-behavior Graph (PMG). Specifically, each node stands for one micro-behavior, and edge weights denote the connection strength. Based on this graph representation, we frame CVR estimation as a graph classification problem over the PMG instances. We propose a novel CVR model, namely, Graph-based Micro-behavior Conversion Model (GMCM), that utilizes Graph Convolutional networks (GCN) to enhance the conventional CVR modeling. In addition, we adopt multi-task learning and inverse propensity weighting to tackle two well-recognized issues in CVR estimation: data sparsity and sample selection bias. Extensive experiments on six large-scale production datasets demonstrate that the proposed methods outperform the state-of-the-art CVR methods under industrial setting.