  Conversion Rate (\emph{CVR}) prediction in modern industrial e-commerce
platforms is becoming increasingly important, which directly contributes to the
final revenue. In order to address the well-known sample selection bias
(\emph{SSB}) and data sparsity (\emph{DS}) issues encountered during CVR
modeling, the abundant labeled macro behaviors ($i.e.$, user's interactions
with items) are used. Nonetheless, we observe that several purchase-related
micro behaviors ($i.e.$, user's interactions with specific components on the
item detail page) can supplement fine-grained cues for \emph{CVR} prediction.
Motivated by this observation, we propose a novel \emph{CVR} prediction method
by Hierarchically Modeling both Micro and Macro behaviors ($HM^3$).
Specifically, we first construct a complete user sequential behavior graph to
hierarchically represent micro behaviors and macro behaviors as one-hop and
two-hop post-click nodes. Then, we embody $HM^3$ as a multi-head deep neural
network, which predicts six probability variables corresponding to explicit
sub-paths in the graph. They are further combined into the prediction targets
of four auxiliary tasks as well as the final $CVR$ according to the conditional
probability rule defined on the graph. By employing multi-task learning and
leveraging the abundant supervisory labels from micro and macro behaviors,
$HM^3$ can be trained end-to-end and address the \emph{SSB} and \emph{DS}
issues. Extensive experiments on both offline and online settings demonstrate
the superiority of the proposed $HM^3$ over representative state-of-the-art
methods.
