  With the increasing variety of services that e-commerce platforms provide,
criteria for evaluating their success become also increasingly multi-targeting.
This work introduces a multi-target optimization framework with Bayesian
modeling of the target events, called Deep Bayesian Multi-Target Learning
(DBMTL). In this framework, target events are modeled as forming a Bayesian
network, in which directed links are parameterized by hidden layers, and
learned from training samples. The structure of Bayesian network is determined
by model selection. We applied the framework to Taobao live-streaming
recommendation, to simultaneously optimize (and strike a balance) on targets
including click-through rate, user stay time in live room, purchasing behaviors
and interactions. Significant improvement has been observed for the proposed
method over other MTL frameworks and the non-MTL model. Our practice shows that
with an integrated causality structure, we can effectively make the learning of
a target benefit from other targets, creating significant synergy effects that
improve all targets. The neural network construction guided by DBMTL fits in
with the general probabilistic model connecting features and multiple targets,
taking weaker assumption than the other methods discussed in this paper. This
theoretical generality brings about practical generalization power over various
targets distributions, including sparse targets and continuous-value ones.
