  To approach different business objectives, online traffic shaping algorithms
aim at improving exposures of a target set of items, such as boosting the
growth of new commodities. Generally, these algorithms assume that the utility
of each user-item pair can be accessed via a well-trained conversion rate
prediction model. However, for real E-Commerce platforms, there are unavoidable
factors preventing us from learning such an accurate model. In order to break
the heavy dependence on accurate inputs of the utility, we propose a general
online traffic shaping protocol for online E-Commerce applications. In our
framework, we approximate the function mapping the bonus scores, which
generally are the only method to influence the ranking result in the traffic
shaping problem, to the numbers of exposures and purchases. Concretely, we
approximate the above function by a class of the piece-wise linear function
constructed on the convex hull of the explored data points. Moreover, we
reformulate the online traffic shaping problem as linear programming where
these piece-wise linear functions are embedded into both the objective and
constraints. Our algorithm can straightforwardly optimize the linear
programming in the prime space, and its solution can be simply applied by a
stochastic strategy to fulfill the optimized objective and the constraints in
expectation. Finally, the online A/B test shows our proposed algorithm steadily
outperforms the previous industrial level traffic shaping algorithm.
