  Modelling the user's multiple behaviors is an essential part of modern
e-commerce, whose widely adopted application is to jointly optimize
click-through rate (CTR) and conversion rate (CVR) predictions. Most of
existing methods overlook the effect of two key characteristics of the user's
behaviors: for each item list, (i) contextual dependence refers to that the
user's behaviors on any item are not purely determinated by the item itself but
also are influenced by the user's previous behaviors (e.g., clicks, purchases)
on other items in the same sequence; (ii) multiple time scales means that users
are likely to click frequently but purchase periodically. To this end, we
develop a new multi-scale user behavior network named Hierarchical rEcurrent
Ranking On the Entire Space (HEROES) which incorporates the contextual
information to estimate the user multiple behaviors in a multi-scale fashion.
Concretely, we introduce a hierarchical framework, where the lower layer models
the user's engagement behaviors while the upper layer estimates the user's
satisfaction behaviors. The proposed architecture can automatically learn a
suitable time scale for each layer to capture the dynamic user's behavioral
patterns. Besides the architecture, we also introduce the Hawkes process to
form a novel recurrent unit which can not only encode the items' features in
the context but also formulate the excitation or discouragement from the user's
previous behaviors. We further show that HEROES can be extended to build
unbiased ranking systems through combinations with the survival analysis
technique. Extensive experiments over three large-scale industrial datasets
demonstrate the superiority of our model compared with the state-of-the-art
methods.
